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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Nature-Inspired Computing</h1><div class="u-tcGrayDark">1,081 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Nature-Inspired Computing</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Nature-Inspired_Computing">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29687756" data-work_id="29687756" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29687756/Optimal_test_sequence_generation_using_firefly_algorithm">Optimal test sequence generation using firefly algorithm</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29687756" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: <a href="http://www.elsevier.com/copyright" rel="nofollow">http://www.elsevier.com/copyright</a> a b s t r a c t Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29687756" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="91de6d971bb0b8eccce8785999c28d68" rel="nofollow" data-download="{"attachment_id":50127395,"asset_id":29687756,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50127395/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29687756 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29687756"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29687756, container: ".js-paper-rank-work_29687756", }); 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The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright a b s t r a c t Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths.","downloadable_attachments":[{"id":50127395,"asset_id":29687756,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":39822,"name":"Software Testing (Computer Science)","url":"https://www.academia.edu/Documents/in/Software_Testing_Computer_Science_?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10380656 coauthored" data-work_id="10380656" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/10380656/Fitness_Based_Position_Update_in_Spider_Monkey_Optimization_Algorithm">Fitness Based Position Update in Spider Monkey Optimization Algorithm</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Spider Monkey Optimization (SMO) technique is most recent member in the family of swarm optimization algorithms.SMO algorithm fall in class of Nature Inspired Algorithm (NIA). SMO algorithm is good in exploration and exploitation of local... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_10380656" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Spider Monkey Optimization (SMO) technique is most recent member in the family of swarm optimization algorithms.SMO algorithm fall in class of Nature Inspired Algorithm (NIA). SMO algorithm is good in exploration and exploitation of local search space and it is well balanced algorithm most of the times. This paper presents a new strategy to update position of solution during local leader phase using fitness of individuals. The proposed algorithm is named as Fitness based Position Update in SMO (FPSMO) algorithm as it updates position of individuals based on their fitness. The anticipated strategy enhances the rate of convergence. The planned FPSMO approach tested over nineteen benchmark functions and for one real world problem so as to establish superiority of it over basic SMO algorithm.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/10380656" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="52f7d72f6c044183ed583d3a3ea2eda2" rel="nofollow" data-download="{"attachment_id":38741676,"asset_id":10380656,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38741676/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4617996" href="https://christuniversity.academia.edu/DrSandeepKumar">Dr. Sandeep Kumar</a><script data-card-contents-for-user="4617996" type="text/json">{"id":4617996,"first_name":"Dr. Sandeep","last_name":"Kumar","domain_name":"christuniversity","page_name":"DrSandeepKumar","display_name":"Dr. Sandeep Kumar","profile_url":"https://christuniversity.academia.edu/DrSandeepKumar?f_ri=84562","photo":"https://0.academia-photos.com/4617996/1929699/11614782/s65_dr_sandeep_kumar.poonia.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-10380656">+1</span><div class="hidden js-additional-users-10380656"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/RajaniPoonia">Rajani Poonia</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-10380656'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-10380656').html(); 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SMO algorithm is good in exploration and exploitation of local search space and it is well balanced algorithm most of the times. This paper presents a new strategy to update position of solution during local leader phase using fitness of individuals. The proposed algorithm is named as Fitness based Position Update in SMO (FPSMO) algorithm as it updates position of individuals based on their fitness. The anticipated strategy enhances the rate of convergence. The planned FPSMO approach tested over nineteen benchmark functions and for one real world problem so as to establish superiority of it over basic SMO algorithm.","downloadable_attachments":[{"id":38741676,"asset_id":10380656,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4617996,"first_name":"Dr. Sandeep","last_name":"Kumar","domain_name":"christuniversity","page_name":"DrSandeepKumar","display_name":"Dr. Sandeep Kumar","profile_url":"https://christuniversity.academia.edu/DrSandeepKumar?f_ri=84562","photo":"https://0.academia-photos.com/4617996/1929699/11614782/s65_dr_sandeep_kumar.poonia.jpg"},{"id":7440583,"first_name":"Rajani","last_name":"Poonia","domain_name":"independent","page_name":"RajaniPoonia","display_name":"Rajani Poonia","profile_url":"https://independent.academia.edu/RajaniPoonia?f_ri=84562","photo":"https://0.academia-photos.com/7440583/2737543/3189105/s65_rajani.poonia.jpg"}],"research_interests":[{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence?f_ri=84562","nofollow":true},{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization?f_ri=84562","nofollow":true},{"id":55523,"name":"Engineering Optimization","url":"https://www.academia.edu/Documents/in/Engineering_Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":1319422,"name":"Population Based Algorithm","url":"https://www.academia.edu/Documents/in/Population_Based_Algorithm?f_ri=84562"},{"id":1319446,"name":"Spider Monkey Optimization Algorithm","url":"https://www.academia.edu/Documents/in/Spider_Monkey_Optimization_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42392504" data-work_id="42392504" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42392504/Multi_objective_Flower_Pollination_Algorithm_MOFPA_">Multi-objective Flower Pollination Algorithm (MOFPA)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">MOFPA--Multi-objective flower pollination algorithm. This demo solves a bi-objective ZDT function of D=30 (dimensions), which can be extended to solve other multi-objective optimization problems. It is relatively straightforward to extend... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42392504" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">MOFPA--Multi-objective flower pollination algorithm. This demo solves a bi-objective ZDT function of D=30 (dimensions), which can be extended to solve other multi-objective optimization problems. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, dimensionality, various parameters, and simple lower and upper bounds (Lb, Ub).<br /><br />X.-S. Yang, M. Karamanoglu, X.-S. He, Flower pollination algorithm: A novel approach for multiobjective optimization, Engineering Optimization, vol. 46, no. 9, 1222-1237 (2014).<br /><br />[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42392504" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="61eaab89e6747d5be85d716f6e1782dd" rel="nofollow" data-download="{"attachment_id":62555717,"asset_id":42392504,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62555717/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_42392504 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42392504"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42392504, container: ".js-paper-rank-work_42392504", }); 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$(".js-view-count[data-work-id=42392504]").text(description); $(".js-view-count-work_42392504").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42392504").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42392504"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="6413" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_">Metaheuristics (Operations Research)</a>, <script data-card-contents-for-ri="6413" type="text/json">{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13445" rel="nofollow" href="https://www.academia.edu/Documents/in/Multiobjective_Optimization">Multiobjective Optimization</a>, <script data-card-contents-for-ri="13445" type="text/json">{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="86588" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristic_Algorithms">Metaheuristic Algorithms</a><script data-card-contents-for-ri="86588" type="text/json">{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42392504]'), work: {"id":42392504,"title":"Multi-objective Flower Pollination Algorithm (MOFPA)","created_at":"2020-03-30T07:37:35.468-07:00","url":"https://www.academia.edu/42392504/Multi_objective_Flower_Pollination_Algorithm_MOFPA_?f_ri=84562","dom_id":"work_42392504","summary":"MOFPA--Multi-objective flower pollination algorithm. This demo solves a bi-objective ZDT function of D=30 (dimensions), which can be extended to solve other multi-objective optimization problems. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, dimensionality, various parameters, and simple lower and upper bounds (Lb, Ub).\n\nX.-S. Yang, M. Karamanoglu, X.-S. He, Flower pollination algorithm: A novel approach for multiobjective optimization, Engineering Optimization, vol. 46, no. 9, 1222-1237 (2014).\n\n[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]","downloadable_attachments":[{"id":62555717,"asset_id":42392504,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true},{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true},{"id":89916,"name":"Multi-Objective Optimization","url":"https://www.academia.edu/Documents/in/Multi-Objective_Optimization?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42680221" data-work_id="42680221" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42680221/The_Firefly_Algorithm_An_Introduction">The Firefly Algorithm: An Introduction</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">This presentation introduces the standard firefly algorithm (FA), which also contains the links to the Matlab code (downloadable at Mathswork File Exchange) and the numerical simulations at Youtube.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42680221" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d6562b36678874c1600ea2fbda97ef43" rel="nofollow" data-download="{"attachment_id":62899461,"asset_id":42680221,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62899461/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_42680221 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42680221"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42680221, container: ".js-paper-rank-work_42680221", }); 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$(".js-view-count[data-work-id=42680221]").text(description); $(".js-view-count-work_42680221").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42680221").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42680221"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="131955" rel="nofollow" href="https://www.academia.edu/Documents/in/Firefly_Aglorithm">Firefly Aglorithm</a>, <script data-card-contents-for-ri="131955" type="text/json">{"id":131955,"name":"Firefly Aglorithm","url":"https://www.academia.edu/Documents/in/Firefly_Aglorithm?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42680221]'), work: {"id":42680221,"title":"The Firefly Algorithm: An Introduction","created_at":"2020-04-09T08:21:57.619-07:00","url":"https://www.academia.edu/42680221/The_Firefly_Algorithm_An_Introduction?f_ri=84562","dom_id":"work_42680221","summary":"This presentation introduces the standard firefly algorithm (FA), which also contains the links to the Matlab code (downloadable at Mathswork File Exchange) and the numerical simulations at Youtube.","downloadable_attachments":[{"id":62899461,"asset_id":42680221,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131955,"name":"Firefly Aglorithm","url":"https://www.academia.edu/Documents/in/Firefly_Aglorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_7395156" data-work_id="7395156" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/7395156/flower_pollination_algorithm_or_flower_algorithm_matlab_code">flower pollination algorithm (or flower algorithm), matlab code</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The standard flower pollination algorithm (FPA) is inspired by the pollination characteristics of flowering plants. This demo solves the Ackley function of D=10 dimensions. It is straightforward to extend it to solve other functions and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_7395156" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The standard flower pollination algorithm (FPA) is inspired by the pollination characteristics of flowering plants. This demo solves the Ackley function of D=10 dimensions. It is straightforward to extend it to solve other functions and optimization problems. <br /> <br />The details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). <a href="https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms" rel="nofollow">https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms</a> <br /> <br />[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7395156" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c0a9fae85c62db2d351b470a736aa18c" rel="nofollow" data-download="{"attachment_id":62552245,"asset_id":7395156,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62552245/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_7395156 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="7395156"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 7395156, container: ".js-paper-rank-work_7395156", }); 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This demo solves the Ackley function of D=10 dimensions. It is straightforward to extend it to solve other functions and optimization problems. \r\n\r\nThe details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms\r\n\r\n[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]","downloadable_attachments":[{"id":62552245,"asset_id":7395156,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562","nofollow":true},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_44209745" data-work_id="44209745" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/44209745/Nature_Inspired_Optimization_Algorithms_Second_Edition">Nature-Inspired Optimization Algorithms: Second Edition</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><a href="https://www.elsevier.com/books/nature-inspired-optimization-algorithms/yang/978-0-12-821986-7" rel="nofollow">https://www.elsevier.com/books/nature-inspired-optimization-algorithms/yang/978-0-12-821986-7</a></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44209745" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8547df4a15d2740f1637cd122f74ee47" rel="nofollow" data-download="{"attachment_id":64574814,"asset_id":44209745,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64574814/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_44209745 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="44209745"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 44209745, container: ".js-paper-rank-work_44209745", }); 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$(".js-view-count[data-work-id=44209745]").text(description); $(".js-view-count-work_44209745").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_44209745").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="44209745"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1701" rel="nofollow" href="https://www.academia.edu/Documents/in/Evolutionary_algorithms">Evolutionary algorithms</a>, <script data-card-contents-for-ri="1701" type="text/json">{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5395" rel="nofollow" href="https://www.academia.edu/Documents/in/Swarm_Intelligence">Swarm Intelligence</a>, <script data-card-contents-for-ri="5395" type="text/json">{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="25773" rel="nofollow" href="https://www.academia.edu/Documents/in/Operations_research_and_Optimization">Operations research and Optimization</a>, <script data-card-contents-for-ri="25773" type="text/json">{"id":25773,"name":"Operations research and Optimization","url":"https://www.academia.edu/Documents/in/Operations_research_and_Optimization?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a><script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=44209745]'), work: {"id":44209745,"title":"Nature-Inspired Optimization Algorithms: Second Edition","created_at":"2020-10-01T09:35:03.403-07:00","url":"https://www.academia.edu/44209745/Nature_Inspired_Optimization_Algorithms_Second_Edition?f_ri=84562","dom_id":"work_44209745","summary":"https://www.elsevier.com/books/nature-inspired-optimization-algorithms/yang/978-0-12-821986-7","downloadable_attachments":[{"id":64574814,"asset_id":44209745,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms?f_ri=84562","nofollow":true},{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence?f_ri=84562","nofollow":true},{"id":25773,"name":"Operations research and Optimization","url":"https://www.academia.edu/Documents/in/Operations_research_and_Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_457296" data-work_id="457296" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/457296/Nature_inspired_metaheuristic_algorithms">Nature-inspired metaheuristic algorithms</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_457296" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/457296" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fe73140a88e9b807e0f9cacf333857ec" rel="nofollow" data-download="{"attachment_id":3368972,"asset_id":457296,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/3368972/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_457296 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="457296"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 457296, container: ".js-paper-rank-work_457296", }); 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$(".js-view-count[data-work-id=457296]").text(description); $(".js-view-count-work_457296").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_457296").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="457296"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="428" rel="nofollow" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a>, <script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2508" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristics_Informatics_">Metaheuristics (Informatics)</a>, <script data-card-contents-for-ri="2508" type="text/json">{"id":2508,"name":"Metaheuristics (Informatics)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Informatics_?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3523" rel="nofollow" href="https://www.academia.edu/Documents/in/Evolutionary_Computation">Evolutionary Computation</a>, <script data-card-contents-for-ri="3523" type="text/json">{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6413" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_">Metaheuristics (Operations Research)</a><script data-card-contents-for-ri="6413" type="text/json">{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=457296]'), work: {"id":457296,"title":"Nature-inspired metaheuristic algorithms","created_at":"2011-02-27T01:21:19.171-08:00","url":"https://www.academia.edu/457296/Nature_inspired_metaheuristic_algorithms?f_ri=84562","dom_id":"work_457296","summary":"Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.","downloadable_attachments":[{"id":3368972,"asset_id":457296,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=84562","nofollow":true},{"id":2508,"name":"Metaheuristics (Informatics)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Informatics_?f_ri=84562","nofollow":true},{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation?f_ri=84562","nofollow":true},{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines?f_ri=84562"},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=84562"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":131955,"name":"Firefly Aglorithm","url":"https://www.academia.edu/Documents/in/Firefly_Aglorithm?f_ri=84562"},{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562"},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562"},{"id":131958,"name":"Metaheuristic Optimization","url":"https://www.academia.edu/Documents/in/Metaheuristic_Optimization?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42688203" data-work_id="42688203" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42688203/Nature_Inspired_Optimization_Algorithms_Introduction_and_Overview">Nature-Inspired Optimization Algorithms: Introduction and Overview</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This presentation introduces the fundamental ideas of nature-inspired optimization algorithms, based on the book by Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier (2014). These slides also contain the links to the Matlab... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42688203" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This presentation introduces the fundamental ideas of nature-inspired optimization algorithms, based on the book by Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier (2014).<br /><br />These slides also contain the links to the Matlab codes at the Matlabcentral  of Mathswork<br /><a href="https://uk.mathworks.com/matlabcentral/profile/authors/3659939-xs-yang" rel="nofollow">https://uk.mathworks.com/matlabcentral/profile/authors/3659939-xs-yang</a><br />The numerical simulations using the Matlab codes are also provided as videos at Youtube<br />and the links are automatically connected within the slides.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div 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href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a>, <script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5026" rel="nofollow" href="https://www.academia.edu/Documents/in/Genetic_Algorithms">Genetic Algorithms</a>, <script data-card-contents-for-ri="5026" type="text/json">{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a 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Elsevier (2014).\n\nThese slides also contain the links to the Matlab codes at the Matlabcentral of Mathswork\nhttps://uk.mathworks.com/matlabcentral/profile/authors/3659939-xs-yang\nThe numerical simulations using the Matlab codes are also provided as videos at Youtube\nand the links are automatically connected within the slides.","downloadable_attachments":[{"id":62898672,"asset_id":42688203,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=84562","nofollow":true},{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true},{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=84562","nofollow":true},{"id":25773,"name":"Operations research and Optimization","url":"https://www.academia.edu/Documents/in/Operations_research_and_Optimization?f_ri=84562"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562"},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562"},{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_7395149" data-work_id="7395149" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/7395149/Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_">Multi-objective Bat Algorithm (MOBA) demo (Matlab code)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_7395149" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub) as well as certain parameters. <br /> <br />Yang, Xin She. “Bat Algorithm for Multi-Objective Optimisation.” International Journal of Bio-Inspired Computation, vol. 3, no. 5, Inderscience Publishers, 2011, p. 267, doi:10.1504/ijbic.2011.042259. <br /> <br />[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7395149" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="86a9599b718fcdd5cd99b2cecf33e1af" rel="nofollow" data-download="{"attachment_id":62552813,"asset_id":7395149,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62552813/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_7395149 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="7395149"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 7395149, container: ".js-paper-rank-work_7395149", }); 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This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub) as well as certain parameters.\r\n\r\nYang, Xin She. “Bat Algorithm for Multi-Objective Optimisation.” International Journal of Bio-Inspired Computation, vol. 3, no. 5, Inderscience Publishers, 2011, p. 267, doi:10.1504/ijbic.2011.042259.\r\n\r\n[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]","downloadable_attachments":[{"id":62552813,"asset_id":7395149,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":898,"name":"Multiculturalism","url":"https://www.academia.edu/Documents/in/Multiculturalism?f_ri=84562","nofollow":true},{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=84562","nofollow":true},{"id":56605,"name":"Multiobjective Evolutionary Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Evolutionary_Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29687618" data-work_id="29687618" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29687618/Discrete_Cuckoo_Search_Applied_to_Job_Shop_Scheduling_Problem">Discrete Cuckoo Search Applied to Job Shop Scheduling Problem</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29687618" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c3906924cb344da06a1f7e120ee53440" rel="nofollow" data-download="{"attachment_id":50127231,"asset_id":29687618,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50127231/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29687618 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29687618"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29687618, container: ".js-paper-rank-work_29687618", }); 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Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/457294" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d5462364929d7fb5a051f9206d8a941b" rel="nofollow" data-download="{"attachment_id":3369511,"asset_id":457294,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/3369511/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_457294 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="457294"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 457294, container: ".js-paper-rank-work_457294", }); 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$(".js-view-count[data-work-id=457294]").text(description); $(".js-view-count-work_457294").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_457294").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="457294"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="47" rel="nofollow" href="https://www.academia.edu/Documents/in/Finance">Finance</a>, <script data-card-contents-for-ri="47" type="text/json">{"id":47,"name":"Finance","url":"https://www.academia.edu/Documents/in/Finance?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="305" rel="nofollow" href="https://www.academia.edu/Documents/in/Applied_Mathematics">Applied Mathematics</a>, <script data-card-contents-for-ri="305" type="text/json">{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="748" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_Economics">Financial Economics</a>, <script data-card-contents-for-ri="748" type="text/json">{"id":748,"name":"Financial Economics","url":"https://www.academia.edu/Documents/in/Financial_Economics?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6413" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_">Metaheuristics (Operations Research)</a><script data-card-contents-for-ri="6413" type="text/json">{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=457294]'), work: {"id":457294,"title":"Engineering Optimization: An Introduction with Metaheuristic Applications","created_at":"2011-02-27T01:16:41.016-08:00","url":"https://www.academia.edu/457294/Engineering_Optimization_An_Introduction_with_Metaheuristic_Applications?f_ri=84562","dom_id":"work_457294","summary":"An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences\n\nFrom engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms.","downloadable_attachments":[{"id":3369511,"asset_id":457294,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":47,"name":"Finance","url":"https://www.academia.edu/Documents/in/Finance?f_ri=84562","nofollow":true},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=84562","nofollow":true},{"id":748,"name":"Financial Economics","url":"https://www.academia.edu/Documents/in/Financial_Economics?f_ri=84562","nofollow":true},{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true},{"id":6908,"name":"Banking","url":"https://www.academia.edu/Documents/in/Banking?f_ri=84562"},{"id":25116,"name":"International Finance","url":"https://www.academia.edu/Documents/in/International_Finance?f_ri=84562"},{"id":25395,"name":"Matlab","url":"https://www.academia.edu/Documents/in/Matlab?f_ri=84562"},{"id":38527,"name":"Optimization (Engineering)","url":"https://www.academia.edu/Documents/in/Optimization_Engineering_?f_ri=84562"},{"id":45591,"name":"Financial Derivatives","url":"https://www.academia.edu/Documents/in/Financial_Derivatives?f_ri=84562"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562"},{"id":87726,"name":"Structural Optimizatoin","url":"https://www.academia.edu/Documents/in/Structural_Optimizatoin?f_ri=84562"},{"id":454037,"name":"Asset and investment valuation","url":"https://www.academia.edu/Documents/in/Asset_and_investment_valuation?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42168390" data-work_id="42168390" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42168390/Navigation_routing_and_nature_inspired_optimization_in_Nature_Inspired_Computation_in_Navigation_and_Routing_Problems">Navigation, routing and nature-inspired optimization, in: Nature-Inspired Computation in Navigation and Routing Problems</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Navigation abilities are crucial for survival in nature, and there are a wide range of sophisticated abilities concerning animal navigation and migration. Many applications are related to navigation and routing problems, which are in turn... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42168390" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Navigation abilities are crucial for survival in nature, and there are a wide range of sophisticated abilities concerning animal navigation and migration. Many applications are related to navigation and routing problems, which are in turn related to optimization problems. This chapter provides an overview of navigation in nature, navigation and routing problems as well as their mathematical formulations. We will then introduce some nature-inspired algorithms for solving optimization problems with discussions about their main characteristics and the ways of solution representations. Citation Detail:</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42168390" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5ad49abcb8686314ef4a3b36a9eb7407" rel="nofollow" data-download="{"attachment_id":62308625,"asset_id":42168390,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62308625/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_42168390 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42168390"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42168390, container: ".js-paper-rank-work_42168390", }); 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Many applications are related to navigation and routing problems, which are in turn related to optimization problems. This chapter provides an overview of navigation in nature, navigation and routing problems as well as their mathematical formulations. We will then introduce some nature-inspired algorithms for solving optimization problems with discussions about their main characteristics and the ways of solution representations. Citation Detail:","downloadable_attachments":[{"id":62308625,"asset_id":42168390,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true},{"id":59695,"name":"Navigation","url":"https://www.academia.edu/Documents/in/Navigation?f_ri=84562","nofollow":true},{"id":60471,"name":"Vehicle Routing Problems","url":"https://www.academia.edu/Documents/in/Vehicle_Routing_Problems?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35298564" data-work_id="35298564" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35298564/Swarm_Intelligence_Past_Present_and_Future">Swarm Intelligence: Past, Present and Future</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35298564" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35298564" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f7c97277903b1d34ef2f70db0704ad30" rel="nofollow" data-download="{"attachment_id":55159179,"asset_id":35298564,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55159179/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_35298564 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35298564"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35298564, container: ".js-paper-rank-work_35298564", }); 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$(".js-view-count[data-work-id=35298564]").text(description); $(".js-view-count-work_35298564").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_35298564").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="35298564"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5395" rel="nofollow" href="https://www.academia.edu/Documents/in/Swarm_Intelligence">Swarm Intelligence</a>, <script data-card-contents-for-ri="5395" type="text/json">{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=35298564]'), work: {"id":35298564,"title":"Swarm Intelligence: Past, Present and Future","created_at":"2017-11-30T04:04:04.071-08:00","url":"https://www.academia.edu/35298564/Swarm_Intelligence_Past_Present_and_Future?f_ri=84562","dom_id":"work_35298564","summary":"Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.","downloadable_attachments":[{"id":55159179,"asset_id":35298564,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35298554" data-work_id="35298554" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35298554/Xin_She_Yang_Editor_Nature_Inspired_Algorithms_and_Applied_Optimization">Xin-She Yang Editor Nature-Inspired Algorithms and Applied Optimization</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35298554" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="006b4d39e182aa70bc4eee0058dbcf35" rel="nofollow" data-download="{"attachment_id":55159165,"asset_id":35298554,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55159165/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_35298554 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35298554"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35298554, container: ".js-paper-rank-work_35298554", }); 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$(".js-view-count[data-work-id=35298554]").text(description); $(".js-view-count-work_35298554").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_35298554").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="35298554"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="3521" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Intelligence">Computational Intelligence</a>, <script data-card-contents-for-ri="3521" type="text/json">{"id":3521,"name":"Computational Intelligence","url":"https://www.academia.edu/Documents/in/Computational_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="176826" rel="nofollow" href="https://www.academia.edu/Documents/in/Firefly_Algorithm">Firefly Algorithm</a>, <script data-card-contents-for-ri="176826" type="text/json">{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=35298554]'), work: {"id":35298554,"title":"Xin-She Yang Editor Nature-Inspired Algorithms and Applied Optimization","created_at":"2017-11-30T04:02:39.705-08:00","url":"https://www.academia.edu/35298554/Xin_She_Yang_Editor_Nature_Inspired_Algorithms_and_Applied_Optimization?f_ri=84562","dom_id":"work_35298554","summary":null,"downloadable_attachments":[{"id":55159165,"asset_id":35298554,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":3521,"name":"Computational Intelligence","url":"https://www.academia.edu/Documents/in/Computational_Intelligence?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":478761,"name":"Applied Optimization","url":"https://www.academia.edu/Documents/in/Applied_Optimization?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_7395352" data-work_id="7395352" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/7395352/Multiobjective_cuckoo_search_for_design_optimization">Multiobjective cuckoo search for design optimization</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_7395352" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may often increase significantly for multiobjective problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we formulate a new cuckoo search for multiobjective optimization. We validate it against a set of multiobjective test functions, and then apply it to solve structural design problems such as beam design and disc brake design. In addition, we also analyze the main characteristics of the algorithm and their implications.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7395352" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0562cefcb91d7504ba1cf84f808b053e" rel="nofollow" data-download="{"attachment_id":33986355,"asset_id":7395352,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/33986355/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_7395352 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="7395352"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 7395352, container: ".js-paper-rank-work_7395352", }); 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$(".js-view-count[data-work-id=7395352]").text(description); $(".js-view-count-work_7395352").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_7395352").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="7395352"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="428" rel="nofollow" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a>, <script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6413" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_">Metaheuristics (Operations Research)</a>, <script data-card-contents-for-ri="6413" type="text/json">{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="43981" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization">Optimization</a>, <script data-card-contents-for-ri="43981" type="text/json">{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a><script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=7395352]'), work: {"id":7395352,"title":"Multiobjective cuckoo search for design optimization","created_at":"2014-06-18T22:54:45.650-07:00","url":"https://www.academia.edu/7395352/Multiobjective_cuckoo_search_for_design_optimization?f_ri=84562","dom_id":"work_7395352","summary":"Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may often increase significantly for multiobjective problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we formulate a new cuckoo search for multiobjective optimization. We validate it against a set of multiobjective test functions, and then apply it to solve structural design problems such as beam design and disc brake design. In addition, we also analyze the main characteristics of the algorithm and their implications.","downloadable_attachments":[{"id":33986355,"asset_id":7395352,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=84562","nofollow":true},{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":810821,"name":"Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Nature_Inspired_Algorithms?f_ri=84562"},{"id":1330965,"name":"Cuckoo Search Algorithm","url":"https://www.academia.edu/Documents/in/Cuckoo_Search_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29687654" data-work_id="29687654" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29687654/Firefly_Algorithm_A_Brief_Review_of_the_Expanding_Literature">Firefly Algorithm: A Brief Review of the Expanding Literature</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Firefly algorithm (FA) was developed by Xin-She Yang in 2008 and it has 1 become an important tool for solving the hardest optimization problems in almost 2 all areas of optimization as well as engineering practice. The literature has... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29687654" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Firefly algorithm (FA) was developed by Xin-She Yang in 2008 and it has 1 become an important tool for solving the hardest optimization problems in almost 2 all areas of optimization as well as engineering practice. The literature has expanded 3 significantly in the last few years. Various FA variants have been developed to suit 4 different applications. This chapter provides a brief review of this expanding and 5 state-of-the-art literature on this dynamic and rapidly evolving domain of swarm 6 intelligence. 7 Keywords Firefly algorithm · Discrete firefly algorithm · Nature-inspired algorithm ·</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29687654" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f14d3832f2481287d025fe09beedb659" rel="nofollow" data-download="{"attachment_id":50127240,"asset_id":29687654,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50127240/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29687654 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29687654"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29687654, container: ".js-paper-rank-work_29687654", }); 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$(".js-view-count[data-work-id=29687654]").text(description); $(".js-view-count-work_29687654").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29687654").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29687654"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="176826" rel="nofollow" href="https://www.academia.edu/Documents/in/Firefly_Algorithm">Firefly Algorithm</a>, <script data-card-contents-for-ri="176826" type="text/json">{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29687654]'), work: {"id":29687654,"title":"Firefly Algorithm: A Brief Review of the Expanding Literature","created_at":"2016-11-05T11:16:54.252-07:00","url":"https://www.academia.edu/29687654/Firefly_Algorithm_A_Brief_Review_of_the_Expanding_Literature?f_ri=84562","dom_id":"work_29687654","summary":"Firefly algorithm (FA) was developed by Xin-She Yang in 2008 and it has 1 become an important tool for solving the hardest optimization problems in almost 2 all areas of optimization as well as engineering practice. The literature has expanded 3 significantly in the last few years. Various FA variants have been developed to suit 4 different applications. This chapter provides a brief review of this expanding and 5 state-of-the-art literature on this dynamic and rapidly evolving domain of swarm 6 intelligence. 7 Keywords Firefly algorithm · Discrete firefly algorithm · Nature-inspired algorithm ·","downloadable_attachments":[{"id":50127240,"asset_id":29687654,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42682955" data-work_id="42682955" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42682955/Flower_Pollination_Algorithm_An_Introduction">Flower Pollination Algorithm: An Introduction</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This presentation explains the fundamental ideas of the standard Flower Pollination Algorithm (FPA), which also contains the links to the free Matlab codes at Mathswork file exchanges and the animations of numerical simulations (video at... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42682955" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This presentation explains the fundamental ideas of the standard Flower Pollination Algorithm (FPA), which also contains the links to the free Matlab codes at Mathswork file exchanges and the animations of numerical simulations (video at Youtube). An example of multi-objective flower pollination algorithm (MOPFA) is also given with link to the Matlab code.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42682955" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3bd178b9051fb96b62768cc96b5a40b7" rel="nofollow" data-download="{"attachment_id":62899444,"asset_id":42682955,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62899444/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_42682955 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42682955"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42682955, container: ".js-paper-rank-work_42682955", }); 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$(".js-view-count[data-work-id=42682955]").text(description); $(".js-view-count-work_42682955").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42682955").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42682955"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="89916" rel="nofollow" href="https://www.academia.edu/Documents/in/Multi-Objective_Optimization">Multi-Objective Optimization</a>, <script data-card-contents-for-ri="89916" type="text/json">{"id":89916,"name":"Multi-Objective Optimization","url":"https://www.academia.edu/Documents/in/Multi-Objective_Optimization?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42682955]'), work: {"id":42682955,"title":"Flower Pollination Algorithm: An Introduction","created_at":"2020-04-09T15:55:02.281-07:00","url":"https://www.academia.edu/42682955/Flower_Pollination_Algorithm_An_Introduction?f_ri=84562","dom_id":"work_42682955","summary":"This presentation explains the fundamental ideas of the standard Flower Pollination Algorithm (FPA), which also contains the links to the free Matlab codes at Mathswork file exchanges and the animations of numerical simulations (video at Youtube). An example of multi-objective flower pollination algorithm (MOPFA) is also given with link to the Matlab code.","downloadable_attachments":[{"id":62899444,"asset_id":42682955,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":89916,"name":"Multi-Objective Optimization","url":"https://www.academia.edu/Documents/in/Multi-Objective_Optimization?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562"},{"id":810821,"name":"Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Nature_Inspired_Algorithms?f_ri=84562"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29684042 coauthored" data-work_id="29684042" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29684042/Attraction_and_Diffusion_in_Nature_Inspired_Optimization_Algorithms">Attraction and Diffusion in Nature-Inspired Optimization Algorithms</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29684042" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29684042" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3fa75585594e9966d2154a68d1795d64" rel="nofollow" data-download="{"attachment_id":50122986,"asset_id":29684042,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50122986/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-29684042">+2</span><div class="hidden js-additional-users-29684042"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/SuashDeb">Suash Deb</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/ThomasHanne">Thomas Hanne</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-29684042'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-29684042').html(); 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_29684042 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="29684042"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 29684042; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=29684042]").text(description); $(".js-view-count-work_29684042").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29684042").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29684042"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="428" rel="nofollow" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a>, <script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="176826" rel="nofollow" href="https://www.academia.edu/Documents/in/Firefly_Algorithm">Firefly Algorithm</a><script data-card-contents-for-ri="176826" type="text/json">{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29684042]'), work: {"id":29684042,"title":"Attraction and Diffusion in Nature-Inspired Optimization Algorithms","created_at":"2016-11-05T06:52:39.809-07:00","url":"https://www.academia.edu/29684042/Attraction_and_Diffusion_in_Nature_Inspired_Optimization_Algorithms?f_ri=84562","dom_id":"work_29684042","summary":"Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research.","downloadable_attachments":[{"id":50122986,"asset_id":29684042,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"},{"id":49008311,"first_name":"Suash","last_name":"Deb","domain_name":"independent","page_name":"SuashDeb","display_name":"Suash Deb","profile_url":"https://independent.academia.edu/SuashDeb?f_ri=84562","photo":"/images/s65_no_pic.png"},{"id":49046405,"first_name":"Thomas","last_name":"Hanne","domain_name":"independent","page_name":"ThomasHanne","display_name":"Thomas Hanne","profile_url":"https://independent.academia.edu/ThomasHanne?f_ri=84562","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=84562","nofollow":true},{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42392351" data-work_id="42392351" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42392351/Multiobjective_Firefly_Algorithm_MOFA_">Multiobjective Firefly Algorithm (MOFA)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The multiobjective firefly algorithm (MOFA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42392351" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The multiobjective firefly algorithm (MOFA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub). Some parameter tuning to vary parameters slightly (such as theta, gamma, and number of iterations) may help improve the quality of the solutions.<br /><br />Yang, Xin-She. “Multiobjective Firefly Algorithm for Continuous Optimization.” Engineering with Computers, vol. 29, no. 2, Springer Science and Business Media LLC, Jan. 2012, pp. 175–84, doi:10.1007/s00366-012-0254-1.<br /><br /><br />[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42392351" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="215eebc256f1e4e0b3d78f6b8919fb81" rel="nofollow" data-download="{"attachment_id":62555293,"asset_id":42392351,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62555293/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_42392351 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42392351"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42392351, container: ".js-paper-rank-work_42392351", }); 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$(".js-view-count[data-work-id=42392351]").text(description); $(".js-view-count-work_42392351").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42392351").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42392351"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="13445" rel="nofollow" href="https://www.academia.edu/Documents/in/Multiobjective_Optimization">Multiobjective Optimization</a>, <script data-card-contents-for-ri="13445" type="text/json">{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="86588" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristic_Algorithms">Metaheuristic Algorithms</a>, <script data-card-contents-for-ri="86588" type="text/json">{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="89916" rel="nofollow" href="https://www.academia.edu/Documents/in/Multi-Objective_Optimization">Multi-Objective Optimization</a><script data-card-contents-for-ri="89916" type="text/json">{"id":89916,"name":"Multi-Objective Optimization","url":"https://www.academia.edu/Documents/in/Multi-Objective_Optimization?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42392351]'), work: {"id":42392351,"title":"Multiobjective Firefly Algorithm (MOFA)","created_at":"2020-03-30T07:33:28.214-07:00","url":"https://www.academia.edu/42392351/Multiobjective_Firefly_Algorithm_MOFA_?f_ri=84562","dom_id":"work_42392351","summary":"The multiobjective firefly algorithm (MOFA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub). Some parameter tuning to vary parameters slightly (such as theta, gamma, and number of iterations) may help improve the quality of the solutions.\n\nYang, Xin-She. “Multiobjective Firefly Algorithm for Continuous Optimization.” Engineering with Computers, vol. 29, no. 2, Springer Science and Business Media LLC, Jan. 2012, pp. 175–84, doi:10.1007/s00366-012-0254-1.\n\n\n[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]","downloadable_attachments":[{"id":62555293,"asset_id":42392351,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true},{"id":89916,"name":"Multi-Objective Optimization","url":"https://www.academia.edu/Documents/in/Multi-Objective_Optimization?f_ri=84562","nofollow":true},{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1739423" data-work_id="1739423" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1739423/Cuckoo_search_algorithm">Cuckoo search algorithm</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The standard cuckoo search algorithm is inspired by the evolutionary characteristics of cuckoo-host interactions. This demo solves a function of d=15 dimensions. It is straightforward to extend it to solve other functions and optimization... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1739423" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The standard cuckoo search algorithm is inspired by the evolutionary characteristics of cuckoo-host interactions. This demo solves a function of d=15 dimensions. It is straightforward to extend it to solve other functions and optimization problems. <br /> <br />The details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). <a href="https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms" rel="nofollow">https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms</a> <br /> <br />[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1739423" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7c64cda68ab80f594dad9d59a0940c84" rel="nofollow" data-download="{"attachment_id":62553751,"asset_id":1739423,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62553751/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_1739423 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1739423"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1739423, container: ".js-paper-rank-work_1739423", }); });</script></li><li class="js-percentile-work_1739423 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1739423; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_1739423"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_1739423 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="1739423"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1739423; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1739423]").text(description); $(".js-view-count-work_1739423").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1739423").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1739423"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="131956" rel="nofollow" href="https://www.academia.edu/Documents/in/Cuckoo_Search">Cuckoo Search</a>, <script data-card-contents-for-ri="131956" type="text/json">{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="278933" rel="nofollow" href="https://www.academia.edu/Documents/in/X._S._Yang_Nature-Inspired_Metaheuristic_Algorithms">X. S. Yang, Nature-Inspired Metaheuristic Algorithms</a>, <script data-card-contents-for-ri="278933" type="text/json">{"id":278933,"name":"X. S. Yang, Nature-Inspired Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/X._S._Yang_Nature-Inspired_Metaheuristic_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1739423]'), work: {"id":1739423,"title":"Cuckoo search algorithm","created_at":"2011-03-01T18:20:26.485-08:00","url":"https://www.academia.edu/1739423/Cuckoo_search_algorithm?f_ri=84562","dom_id":"work_1739423","summary":"The standard cuckoo search algorithm is inspired by the evolutionary characteristics of cuckoo-host interactions. This demo solves a function of d=15 dimensions. It is straightforward to extend it to solve other functions and optimization problems.\r\n\r\nThe details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms\r\n\r\n[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]","downloadable_attachments":[{"id":62553751,"asset_id":1739423,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true},{"id":278933,"name":"X. S. Yang, Nature-Inspired Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/X._S._Yang_Nature-Inspired_Metaheuristic_Algorithms?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":1330965,"name":"Cuckoo Search Algorithm","url":"https://www.academia.edu/Documents/in/Cuckoo_Search_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12235909" data-work_id="12235909" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/12235909/HYBRID_DATA_CLUSTERING_APPROACH_USING_K_MEANS_AND_FLOWER_POLLINATION_ALGORITHM">HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHM</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12235909" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering <br />algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers <br />from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global <br />optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental <br />results, FPAKM is better than FPA and K-Means.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12235909" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8a6492e995ce370577ebe01e41029279" rel="nofollow" data-download="{"attachment_id":37529518,"asset_id":12235909,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37529518/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="21895691" rel="nofollow" href="https://independent.academia.edu/AciiJournal">Advanced Computational Intelligence: An International Journal (ACII)</a><script data-card-contents-for-user="21895691" type="text/json">{"id":21895691,"first_name":"Advanced Computational Intelligence: An International Journal","last_name":"(ACII)","domain_name":"independent","page_name":"AciiJournal","display_name":"Advanced Computational Intelligence: An International Journal (ACII)","profile_url":"https://independent.academia.edu/AciiJournal?f_ri=84562","photo":"https://0.academia-photos.com/21895691/7727776/60965479/s65_advanced_computational_intelligence_an_international_journal._acii_.png"}</script></span></span></li><li class="js-paper-rank-work_12235909 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12235909"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12235909, container: ".js-paper-rank-work_12235909", }); 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$(".js-view-count[data-work-id=12235909]").text(description); $(".js-view-count-work_12235909").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_12235909").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="12235909"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">18</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="3703" rel="nofollow" href="https://www.academia.edu/Documents/in/Network_Security">Network Security</a>, <script data-card-contents-for-ri="3703" type="text/json">{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5395" rel="nofollow" href="https://www.academia.edu/Documents/in/Swarm_Intelligence">Swarm Intelligence</a>, <script data-card-contents-for-ri="5395" type="text/json">{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="21903" rel="nofollow" href="https://www.academia.edu/Documents/in/Encryption">Encryption</a>, <script data-card-contents-for-ri="21903" type="text/json">{"id":21903,"name":"Encryption","url":"https://www.academia.edu/Documents/in/Encryption?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="29831" rel="nofollow" href="https://www.academia.edu/Documents/in/Cluster_Analysis_Multivariate_Data_Analysis_">Cluster Analysis (Multivariate Data Analysis)</a><script data-card-contents-for-ri="29831" type="text/json">{"id":29831,"name":"Cluster Analysis (Multivariate Data Analysis)","url":"https://www.academia.edu/Documents/in/Cluster_Analysis_Multivariate_Data_Analysis_?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=12235909]'), work: {"id":12235909,"title":"HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHM","created_at":"2015-05-04T22:03:11.427-07:00","url":"https://www.academia.edu/12235909/HYBRID_DATA_CLUSTERING_APPROACH_USING_K_MEANS_AND_FLOWER_POLLINATION_ALGORITHM?f_ri=84562","dom_id":"work_12235909","summary":"Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering\r\nalgorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers\r\nfrom initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global\r\noptimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental\r\nresults, FPAKM is better than FPA and K-Means.","downloadable_attachments":[{"id":37529518,"asset_id":12235909,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":21895691,"first_name":"Advanced Computational Intelligence: An International Journal","last_name":"(ACII)","domain_name":"independent","page_name":"AciiJournal","display_name":"Advanced Computational Intelligence: An International Journal (ACII)","profile_url":"https://independent.academia.edu/AciiJournal?f_ri=84562","photo":"https://0.academia-photos.com/21895691/7727776/60965479/s65_advanced_computational_intelligence_an_international_journal._acii_.png"}],"research_interests":[{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security?f_ri=84562","nofollow":true},{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence?f_ri=84562","nofollow":true},{"id":21903,"name":"Encryption","url":"https://www.academia.edu/Documents/in/Encryption?f_ri=84562","nofollow":true},{"id":29831,"name":"Cluster Analysis (Multivariate Data Analysis)","url":"https://www.academia.edu/Documents/in/Cluster_Analysis_Multivariate_Data_Analysis_?f_ri=84562","nofollow":true},{"id":38288,"name":"Optical wireless Communications","url":"https://www.academia.edu/Documents/in/Optical_wireless_Communications?f_ri=84562"},{"id":51449,"name":"K-means","url":"https://www.academia.edu/Documents/in/K-means?f_ri=84562"},{"id":62225,"name":"Fuzzy Logic Programming","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic_Programming?f_ri=84562"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":106108,"name":"Swarm Robotics,Industrial Robotics, Mobile Robotics,Bionics, Assistive Robotics, Automation, Machine vision, Artificial Intelligence, PLC, Control Systems","url":"https://www.academia.edu/Documents/in/Swarm_Robotics_Industrial_Robotics_Mobile_Robotics_Bionics_Assistive_Robotics_Automation_Machine?f_ri=84562"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis?f_ri=84562"},{"id":141114,"name":"World Wide Web","url":"https://www.academia.edu/Documents/in/World_Wide_Web?f_ri=84562"},{"id":332654,"name":"Graph Partitioning","url":"https://www.academia.edu/Documents/in/Graph_Partitioning?f_ri=84562"},{"id":627850,"name":"K Means","url":"https://www.academia.edu/Documents/in/K_Means?f_ri=84562"},{"id":986280,"name":"Normalized Cut","url":"https://www.academia.edu/Documents/in/Normalized_Cut?f_ri=84562"},{"id":1032324,"name":"K means Clustering","url":"https://www.academia.edu/Documents/in/K_means_Clustering?f_ri=84562"},{"id":1122287,"name":"Similarity Metric","url":"https://www.academia.edu/Documents/in/Similarity_Metric?f_ri=84562"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562"},{"id":2904834,"name":"Eigenvalue Decomposition","url":"https://www.academia.edu/Documents/in/Eigenvalue_Decomposition?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29687609" data-work_id="29687609" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29687609/Cuckoo_Search_From_Cuckoo_Reproduction_Strategy_to_Combinatorial_Optimization">Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial Optimization</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Combinatorial optimization problems, specially those that are NP-hard, are increasingly being dealt with by stochastic, metaheuristic approaches. Most recently developed metaheuristics are nature-inspired and they are often inspired by... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29687609" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Combinatorial optimization problems, specially those that are NP-hard, are increasingly being dealt with by stochastic, metaheuristic approaches. Most recently developed metaheuristics are nature-inspired and they are often inspired by some special characteristics in evolution, ecological or biological systems. This chapter discusses how to go from a biological phenomenon such as the aggressive reproduction strategy of cuckoos to solve tough problems in the combinatorial search space. Key features and steps are highlighted, together with the discussions of further research topics.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29687609" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="045fa4ae435f1e2448b9f0dc57d17d7d" rel="nofollow" data-download="{"attachment_id":50127197,"asset_id":29687609,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50127197/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29687609 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29687609"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29687609, container: ".js-paper-rank-work_29687609", }); });</script></li><li class="js-percentile-work_29687609 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 29687609; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_29687609"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_29687609 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="29687609"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 29687609; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=29687609]").text(description); $(".js-view-count-work_29687609").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29687609").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29687609"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="131956" rel="nofollow" href="https://www.academia.edu/Documents/in/Cuckoo_Search">Cuckoo Search</a>, <script data-card-contents-for-ri="131956" type="text/json">{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a>, <script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1330965" rel="nofollow" href="https://www.academia.edu/Documents/in/Cuckoo_Search_Algorithm">Cuckoo Search Algorithm</a><script data-card-contents-for-ri="1330965" type="text/json">{"id":1330965,"name":"Cuckoo Search Algorithm","url":"https://www.academia.edu/Documents/in/Cuckoo_Search_Algorithm?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29687609]'), work: {"id":29687609,"title":"Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial Optimization","created_at":"2016-11-05T11:13:04.268-07:00","url":"https://www.academia.edu/29687609/Cuckoo_Search_From_Cuckoo_Reproduction_Strategy_to_Combinatorial_Optimization?f_ri=84562","dom_id":"work_29687609","summary":"Combinatorial optimization problems, specially those that are NP-hard, are increasingly being dealt with by stochastic, metaheuristic approaches. Most recently developed metaheuristics are nature-inspired and they are often inspired by some special characteristics in evolution, ecological or biological systems. This chapter discusses how to go from a biological phenomenon such as the aggressive reproduction strategy of cuckoos to solve tough problems in the combinatorial search space. Key features and steps are highlighted, together with the discussions of further research topics.","downloadable_attachments":[{"id":50127197,"asset_id":29687609,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":1330965,"name":"Cuckoo Search Algorithm","url":"https://www.academia.edu/Documents/in/Cuckoo_Search_Algorithm?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29687666" data-work_id="29687666" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29687666/Improved_cuckoo_search_algorithm_for_hybrid_flow_shop_scheduling_problems_to_minimize_makespan">Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29687666" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29687666" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c84319ecd8b9f616c022bc9139ce19d7" rel="nofollow" data-download="{"attachment_id":50127255,"asset_id":29687666,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50127255/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29687666 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29687666"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29687666, container: ".js-paper-rank-work_29687666", }); });</script></li><li class="js-percentile-work_29687666 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 29687666; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_29687666"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_29687666 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="29687666"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 29687666; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=29687666]").text(description); $(".js-view-count-work_29687666").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29687666").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29687666"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="9049" rel="nofollow" href="https://www.academia.edu/Documents/in/Flow_Shop_Scheduling">Flow Shop Scheduling</a>, <script data-card-contents-for-ri="9049" type="text/json">{"id":9049,"name":"Flow Shop Scheduling","url":"https://www.academia.edu/Documents/in/Flow_Shop_Scheduling?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="131956" rel="nofollow" href="https://www.academia.edu/Documents/in/Cuckoo_Search">Cuckoo Search</a>, <script data-card-contents-for-ri="131956" type="text/json">{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29687666]'), work: {"id":29687666,"title":"Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan","created_at":"2016-11-05T11:18:53.610-07:00","url":"https://www.academia.edu/29687666/Improved_cuckoo_search_algorithm_for_hybrid_flow_shop_scheduling_problems_to_minimize_makespan?f_ri=84562","dom_id":"work_29687666","summary":"The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics.","downloadable_attachments":[{"id":50127255,"asset_id":29687666,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":9049,"name":"Flow Shop Scheduling","url":"https://www.academia.edu/Documents/in/Flow_Shop_Scheduling?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1739424" data-work_id="1739424" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1739424/Firefly_Algorithm">Firefly Algorithm </a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The standard firefly algorithm is inspired by the flashing patterns of tropical fireflies. This demo solves a function of d=10 dimensions. It is straightforward to extend it to solve other functions and optimization problems. The... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1739424" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The standard firefly algorithm is inspired by the flashing patterns of tropical fireflies. This demo solves a function of d=10 dimensions. It is straightforward to extend it to solve other functions and optimization problems. <br /> <br />The details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). <a href="https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms" rel="nofollow">https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms</a> <br /> <br />[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1739424" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="dc539bdffd29e92675ab8a6896e89dd1" rel="nofollow" data-download="{"attachment_id":62553677,"asset_id":1739424,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62553677/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_1739424 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1739424"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1739424, container: ".js-paper-rank-work_1739424", }); });</script></li><li class="js-percentile-work_1739424 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1739424; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_1739424"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_1739424 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="1739424"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1739424; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1739424]").text(description); $(".js-view-count-work_1739424").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1739424").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1739424"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="6413" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_">Metaheuristics (Operations Research)</a>, <script data-card-contents-for-ri="6413" type="text/json">{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="86588" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristic_Algorithms">Metaheuristic Algorithms</a>, <script data-card-contents-for-ri="86588" type="text/json">{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="176826" rel="nofollow" href="https://www.academia.edu/Documents/in/Firefly_Algorithm">Firefly Algorithm</a><script data-card-contents-for-ri="176826" type="text/json">{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1739424]'), work: {"id":1739424,"title":"Firefly Algorithm ","created_at":"2011-03-01T18:22:39.487-08:00","url":"https://www.academia.edu/1739424/Firefly_Algorithm?f_ri=84562","dom_id":"work_1739424","summary":"The standard firefly algorithm is inspired by the flashing patterns of tropical fireflies. This demo solves a function of d=10 dimensions. It is straightforward to extend it to solve other functions and optimization problems.\r\n\r\nThe details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms\r\n\r\n[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]","downloadable_attachments":[{"id":62553677,"asset_id":1739424,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true},{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_33995639" data-work_id="33995639" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/33995639/Comparison_of_bio_inspired_algorithms_applied_to_the_coordination_of_mobile_robots_considering_the_energy_consumption">Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_33995639" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue some distributed targets. This problem is formulated by first defining an optimization model and then considering two sub-problems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit more robots to carry out the disarm task together. For this purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/33995639" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e791bdb805a1b0491a2cd6c48821cd1c" rel="nofollow" data-download="{"attachment_id":53944717,"asset_id":33995639,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53944717/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_33995639 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="33995639"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 33995639, container: ".js-paper-rank-work_33995639", }); 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$(".js-view-count[data-work-id=33995639]").text(description); $(".js-view-count-work_33995639").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_33995639").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="33995639"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="77" rel="nofollow" href="https://www.academia.edu/Documents/in/Robotics">Robotics</a>, <script data-card-contents-for-ri="77" type="text/json">{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="176826" rel="nofollow" href="https://www.academia.edu/Documents/in/Firefly_Algorithm">Firefly Algorithm</a><script data-card-contents-for-ri="176826" type="text/json">{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=33995639]'), work: {"id":33995639,"title":"Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption","created_at":"2017-07-22T06:26:22.550-07:00","url":"https://www.academia.edu/33995639/Comparison_of_bio_inspired_algorithms_applied_to_the_coordination_of_mobile_robots_considering_the_energy_consumption?f_ri=84562","dom_id":"work_33995639","summary":"Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue some distributed targets. This problem is formulated by first defining an optimization model and then considering two sub-problems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit more robots to carry out the disarm task together. For this purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete","downloadable_attachments":[{"id":53944717,"asset_id":33995639,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=84562","nofollow":true},{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36800267" data-work_id="36800267" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36800267/Reliability_based_design_optimization_using_the_directional_bat_algorithm">Reliability based-design optimization using the directional bat algorithm</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Reliability based design optimization (RBDO) problems are important in engineering applications, but it is challenging to solve such problems. In this study, a new resolution method based on the directional Bat Algorithm (dBA) is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36800267" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Reliability based design optimization (RBDO) problems are important in engineering applications, but it is challenging to solve such problems. In this study, a new resolution method based on the directional Bat Algorithm (dBA) is presented. To overcome the difficulties in the evaluations of probabilistic constraints, the reliable design space concept has been applied to convert the yielded stochastic constrained optimization problem from the RBDO formulation into a deterministic constrained optimization problem. In addition, the constraint handling technique has also been introduced to the dBA so that the algorithm can solve constrained optimization problem effectively. The new method has been applied to several engineering problems and the results show that the new method can solve different varieties of RBDO problems efficiently. In fact, the obtained solutions are consistent with the best results in the literature.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36800267" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d6b9da7aab40773ad2aacf2debfd8c61" rel="nofollow" data-download="{"attachment_id":56749240,"asset_id":36800267,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56749240/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_36800267 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36800267"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36800267, container: ".js-paper-rank-work_36800267", }); 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$(".js-view-count[data-work-id=36800267]").text(description); $(".js-view-count-work_36800267").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36800267").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36800267"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="35232" rel="nofollow" href="https://www.academia.edu/Documents/in/Reliability_Engineering">Reliability Engineering</a>, <script data-card-contents-for-ri="35232" type="text/json">{"id":35232,"name":"Reliability Engineering","url":"https://www.academia.edu/Documents/in/Reliability_Engineering?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="131957" rel="nofollow" href="https://www.academia.edu/Documents/in/Bat_Algorithm">Bat Algorithm</a>, <script data-card-contents-for-ri="131957" type="text/json">{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36800267]'), work: {"id":36800267,"title":"Reliability based-design optimization using the directional bat algorithm","created_at":"2018-06-07T08:14:39.307-07:00","url":"https://www.academia.edu/36800267/Reliability_based_design_optimization_using_the_directional_bat_algorithm?f_ri=84562","dom_id":"work_36800267","summary":"Reliability based design optimization (RBDO) problems are important in engineering applications, but it is challenging to solve such problems. In this study, a new resolution method based on the directional Bat Algorithm (dBA) is presented. To overcome the difficulties in the evaluations of probabilistic constraints, the reliable design space concept has been applied to convert the yielded stochastic constrained optimization problem from the RBDO formulation into a deterministic constrained optimization problem. In addition, the constraint handling technique has also been introduced to the dBA so that the algorithm can solve constrained optimization problem effectively. The new method has been applied to several engineering problems and the results show that the new method can solve different varieties of RBDO problems efficiently. In fact, the obtained solutions are consistent with the best results in the literature.","downloadable_attachments":[{"id":56749240,"asset_id":36800267,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":35232,"name":"Reliability Engineering","url":"https://www.academia.edu/Documents/in/Reliability_Engineering?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":1389141,"name":"Bat-inspired Optimization Algorithm","url":"https://www.academia.edu/Documents/in/Bat-inspired_Optimization_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16867390 coauthored" data-work_id="16867390" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16867390/New_Travelling_Wave_Solutions_of_Two_Nonlinear_Physical_Models_by_Using_a_Modified_Tan_Coth_Method">New Travelling Wave Solutions of Two Nonlinear Physical Models by Using a Modified Tan-Coth Method</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this work, a modied tanh􀀀coth method is used to derive travelling wave solutions for (2 + 1)- dimensional Zakharov-Kuznetsov (ZK) equation and (3 + 1)-dimensional Burgers equation. A new variable is used to solve these equations and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16867390" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this work, a modied tanh􀀀coth method is used to derive travelling wave solutions for (2 + 1)- dimensional Zakharov-Kuznetsov (ZK) equation and (3 + 1)-dimensional Burgers equation. A new variable is used to solve these equations and establish new travelling wave solutions.<br /><br />Keywords: tanh-coth method; travelling wave solution; (2 + 1)-dimensional Zakharov-Kuznetsov equation;<br />(3 + 1)-dimensional Burgers equation</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16867390" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fac4198789efa5b99d42b12f897493d9" rel="nofollow" data-download="{"attachment_id":39227157,"asset_id":16867390,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/39227157/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="29803402" href="https://essex.academia.edu/AbdellahSalhi">Abdellah Salhi</a><script data-card-contents-for-user="29803402" type="text/json">{"id":29803402,"first_name":"Abdellah","last_name":"Salhi","domain_name":"essex","page_name":"AbdellahSalhi","display_name":"Abdellah Salhi","profile_url":"https://essex.academia.edu/AbdellahSalhi?f_ri=84562","photo":"https://0.academia-photos.com/29803402/9785312/10902477/s65_abdellah.salhi.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-16867390">+1</span><div class="hidden js-additional-users-16867390"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/%C3%96merG%C3%B6z%C3%BCk%C4%B1z%C4%B1l">Ömer Gözükızıl</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-16867390'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-16867390').html(); 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A new variable is used to solve these equations and establish new travelling wave solutions.\n\nKeywords: tanh-coth method; travelling wave solution; (2 + 1)-dimensional Zakharov-Kuznetsov equation;\n(3 + 1)-dimensional Burgers equation","downloadable_attachments":[{"id":39227157,"asset_id":16867390,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":29803402,"first_name":"Abdellah","last_name":"Salhi","domain_name":"essex","page_name":"AbdellahSalhi","display_name":"Abdellah Salhi","profile_url":"https://essex.academia.edu/AbdellahSalhi?f_ri=84562","photo":"https://0.academia-photos.com/29803402/9785312/10902477/s65_abdellah.salhi.jpg"},{"id":36457792,"first_name":"Ömer","last_name":"Gözükızıl","domain_name":"independent","page_name":"ÖmerGözükızıl","display_name":"Ömer Gözükızıl","profile_url":"https://independent.academia.edu/%C3%96merG%C3%B6z%C3%BCk%C4%B1z%C4%B1l?f_ri=84562","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":377,"name":"Partial Differential Equations","url":"https://www.academia.edu/Documents/in/Partial_Differential_Equations?f_ri=84562","nofollow":true},{"id":23922,"name":"Dynamical systems and Chaos","url":"https://www.academia.edu/Documents/in/Dynamical_systems_and_Chaos?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":97292,"name":"Optimisation","url":"https://www.academia.edu/Documents/in/Optimisation?f_ri=84562","nofollow":true},{"id":2182312,"name":"Plant Propagation Algorithms","url":"https://www.academia.edu/Documents/in/Plant_Propagation_Algorithms?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36758359" data-work_id="36758359" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36758359/POSTDOC_THE_HUMAN_OPTIMIZATION">POSTDOC : THE HUMAN OPTIMIZATION</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper is dedicated to everyone who is interested in the Artificial Intelligence. John Henry Holland proposed Genetic Algorithm in the early 1970s. Ant Colony Optimization was proposed by Marco Dorigo in 1992. Particle Swarm... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36758359" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper is dedicated to everyone who is interested in the Artificial Intelligence. John Henry Holland proposed Genetic Algorithm in the early 1970s. Ant Colony Optimization was proposed by Marco Dorigo in 1992. Particle Swarm Optimization was introduced by Kennedy and Eberhart in 1995. Storn and Price introduced Differential Evolution in 1996. K.M. Passino introduced Bacterial Foraging Optimization Algorithm in 2002. In 2003, X.L. Li proposed Artificial Fish Swarm Algorithm. Artificial Bee Colony algorithm was introduced by Karaboga in 2005. In the past, researchers have explored behavior of chromosomes, birds, fishes, ants, bacteria, bees and so on to create excellent optimization methods for solving complex optimization problems. In this paper, Satish Gajawada proposed The Human Optimization. Humans progressed like anything. They help each other. There are so many plus points in Humans. In fact all optimization algorithms based on other beings are created by Humans. There is so much to explore in behavior of Human for creating awesome optimization algorithms. Artificial Fishes, birds, ants, bees etc have solved optimization problems. Similarly, optimization method based on Humans is expected to solve complex problems. This paper sets the trend for all optimization algorithms that come in future based on Humans.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36758359" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7cfb47a65be497eb4e27bb42e78da185" rel="nofollow" data-download="{"attachment_id":56704769,"asset_id":36758359,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56704769/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="82365886" href="https://iitr-in.academia.edu/SatishGajawada">Satish Gajawada</a><script data-card-contents-for-user="82365886" type="text/json">{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"}</script></span></span></li><li class="js-paper-rank-work_36758359 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36758359"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36758359, container: ".js-paper-rank-work_36758359", }); 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$(".js-view-count[data-work-id=36758359]").text(description); $(".js-view-count-work_36758359").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36758359").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36758359"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">14</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5026" rel="nofollow" href="https://www.academia.edu/Documents/in/Genetic_Algorithms">Genetic Algorithms</a>, <script data-card-contents-for-ri="5026" type="text/json">{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6420" rel="nofollow" href="https://www.academia.edu/Documents/in/Ant_Colony_Optimization">Ant Colony Optimization</a><script data-card-contents-for-ri="6420" type="text/json">{"id":6420,"name":"Ant Colony Optimization","url":"https://www.academia.edu/Documents/in/Ant_Colony_Optimization?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36758359]'), work: {"id":36758359,"title":"POSTDOC : THE HUMAN OPTIMIZATION","created_at":"2018-06-01T02:40:20.852-07:00","url":"https://www.academia.edu/36758359/POSTDOC_THE_HUMAN_OPTIMIZATION?f_ri=84562","dom_id":"work_36758359","summary":"This paper is dedicated to everyone who is interested in the Artificial Intelligence. John Henry Holland proposed Genetic Algorithm in the early 1970s. Ant Colony Optimization was proposed by Marco Dorigo in 1992. Particle Swarm Optimization was introduced by Kennedy and Eberhart in 1995. Storn and Price introduced Differential Evolution in 1996. K.M. Passino introduced Bacterial Foraging Optimization Algorithm in 2002. In 2003, X.L. Li proposed Artificial Fish Swarm Algorithm. Artificial Bee Colony algorithm was introduced by Karaboga in 2005. In the past, researchers have explored behavior of chromosomes, birds, fishes, ants, bacteria, bees and so on to create excellent optimization methods for solving complex optimization problems. In this paper, Satish Gajawada proposed The Human Optimization. Humans progressed like anything. They help each other. There are so many plus points in Humans. In fact all optimization algorithms based on other beings are created by Humans. There is so much to explore in behavior of Human for creating awesome optimization algorithms. Artificial Fishes, birds, ants, bees etc have solved optimization problems. Similarly, optimization method based on Humans is expected to solve complex problems. This paper sets the trend for all optimization algorithms that come in future based on Humans.","downloadable_attachments":[{"id":56704769,"asset_id":36758359,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=84562","nofollow":true},{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true},{"id":6420,"name":"Ant Colony Optimization","url":"https://www.academia.edu/Documents/in/Ant_Colony_Optimization?f_ri=84562","nofollow":true},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution?f_ri=84562"},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization?f_ri=84562"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":112657,"name":"Artificial Bee Colony Algorithm","url":"https://www.academia.edu/Documents/in/Artificial_Bee_Colony_Algorithm?f_ri=84562"},{"id":154208,"name":"Bio-Inspired Computing","url":"https://www.academia.edu/Documents/in/Bio-Inspired_Computing?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":1389134,"name":"Bacterial Foraging Optimization Algorithm","url":"https://www.academia.edu/Documents/in/Bacterial_Foraging_Optimization_Algorithm?f_ri=84562"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562"},{"id":1956464,"name":"Artificial Fish Swarm Algorithm","url":"https://www.academia.edu/Documents/in/Artificial_Fish_Swarm_Algorithm?f_ri=84562"},{"id":2759061,"name":"Artificial Human Optimization","url":"https://www.academia.edu/Documents/in/Artificial_Human_Optimization?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43287331 coauthored" data-work_id="43287331" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43287331/Artificial_Satisfaction_The_Brother_of_Artificial_Intelligence">Artificial Satisfaction -The Brother of Artificial Intelligence</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">Please read the paper to know the abstract.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43287331" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="074b03509e5801ca0423b49af335644a" rel="nofollow" data-download="{"attachment_id":63563546,"asset_id":43287331,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63563546/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="82365886" href="https://iitr-in.academia.edu/SatishGajawada">Satish Gajawada</a><script data-card-contents-for-user="82365886" type="text/json">{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-43287331">+1</span><div class="hidden js-additional-users-43287331"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a rel="nofollow" href="https://independent.academia.edu/HassanMoustafa5">Hassan M H Moustafa</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-43287331'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-43287331').html(); } } new 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Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2674" rel="nofollow" href="https://www.academia.edu/Documents/in/Intelligence">Intelligence</a><script data-card-contents-for-ri="2674" type="text/json">{"id":2674,"name":"Intelligence","url":"https://www.academia.edu/Documents/in/Intelligence?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43287331]'), work: {"id":43287331,"title":"Artificial Satisfaction -The Brother of Artificial Intelligence","created_at":"2020-06-08T06:12:40.863-07:00","url":"https://www.academia.edu/43287331/Artificial_Satisfaction_The_Brother_of_Artificial_Intelligence?f_ri=84562","dom_id":"work_43287331","summary":"Please read the paper to know the abstract.","downloadable_attachments":[{"id":63563546,"asset_id":43287331,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"},{"id":43092496,"first_name":"Hassan","last_name":"Moustafa","domain_name":"independent","page_name":"HassanMoustafa5","display_name":"Hassan M H Moustafa","profile_url":"https://independent.academia.edu/HassanMoustafa5?f_ri=84562","photo":"https://0.academia-photos.com/43092496/30224069/28035701/s65_hassan.moustafa.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=84562","nofollow":true},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=84562","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true},{"id":2674,"name":"Intelligence","url":"https://www.academia.edu/Documents/in/Intelligence?f_ri=84562","nofollow":true},{"id":10700,"name":"Satisfaction","url":"https://www.academia.edu/Documents/in/Satisfaction?f_ri=84562"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":142872,"name":"John McCarthy","url":"https://www.academia.edu/Documents/in/John_McCarthy?f_ri=84562"},{"id":154208,"name":"Bio-Inspired Computing","url":"https://www.academia.edu/Documents/in/Bio-Inspired_Computing?f_ri=84562"},{"id":246349,"name":"Lotfi Zadeh","url":"https://www.academia.edu/Documents/in/Lotfi_Zadeh?f_ri=84562"},{"id":418578,"name":"New Creation","url":"https://www.academia.edu/Documents/in/New_Creation?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42168404" data-work_id="42168404" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42168404/Nature_Inspired_Optimization_Algorithms_Challenges_and_Open_Problems">Nature-Inspired Optimization Algorithms: Challenges and Open Problems</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Citation Detail: Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42168404" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Citation Detail: Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42168404" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="15548bfb38d36e39f10c0931bfed8d2d" rel="nofollow" data-download="{"attachment_id":62308640,"asset_id":42168404,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62308640/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_42168404 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42168404"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42168404, container: ".js-paper-rank-work_42168404", }); });</script></li><li class="js-percentile-work_42168404 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42168404; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_42168404"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_42168404 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="42168404"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42168404; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=42168404]").text(description); $(".js-view-count-work_42168404").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42168404").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42168404"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="3853" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_Mathematics_">Optimization (Mathematics)</a>, <script data-card-contents-for-ri="3853" type="text/json">{"id":3853,"name":"Optimization (Mathematics)","url":"https://www.academia.edu/Documents/in/Optimization_Mathematics_?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6413" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_">Metaheuristics (Operations Research)</a>, <script data-card-contents-for-ri="6413" type="text/json">{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="86588" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristic_Algorithms">Metaheuristic Algorithms</a><script data-card-contents-for-ri="86588" type="text/json">{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42168404]'), work: {"id":42168404,"title":"Nature-Inspired Optimization Algorithms: Challenges and Open Problems","created_at":"2020-03-08T08:20:36.610-07:00","url":"https://www.academia.edu/42168404/Nature_Inspired_Optimization_Algorithms_Challenges_and_Open_Problems?f_ri=84562","dom_id":"work_42168404","summary":"Citation Detail: Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research.","downloadable_attachments":[{"id":62308640,"asset_id":42168404,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":3853,"name":"Optimization (Mathematics)","url":"https://www.academia.edu/Documents/in/Optimization_Mathematics_?f_ri=84562","nofollow":true},{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":423244,"name":"Evolutionary Metaheuristics","url":"https://www.academia.edu/Documents/in/Evolutionary_Metaheuristics?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29684238" data-work_id="29684238" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29684238/Flower_Pollination_Algorithm_A_Novel_Approach_for_Multiobjective_Optimization">Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this paper, the recently developed flower... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29684238" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this paper, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of mul-tobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis are highlighted and discussed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29684238" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7275a5e3c88a77fb1c42442177d45940" rel="nofollow" data-download="{"attachment_id":50123016,"asset_id":29684238,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50123016/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29684238 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29684238"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29684238, container: ".js-paper-rank-work_29684238", }); 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$(".js-view-count[data-work-id=29684238]").text(description); $(".js-view-count-work_29684238").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29684238").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29684238"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a>, <script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1421559" rel="nofollow" href="https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm">Flower Pollination Algorithm</a><script data-card-contents-for-ri="1421559" type="text/json">{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29684238]'), work: {"id":29684238,"title":"Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization","created_at":"2016-11-05T06:57:14.570-07:00","url":"https://www.academia.edu/29684238/Flower_Pollination_Algorithm_A_Novel_Approach_for_Multiobjective_Optimization?f_ri=84562","dom_id":"work_29684238","summary":"Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this paper, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of mul-tobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis are highlighted and discussed.","downloadable_attachments":[{"id":50123016,"asset_id":29684238,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29684080 coauthored" data-work_id="29684080" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29684080/Nature_Inspired_Framework_for_Hyperspectral_Band_Selection">Nature-Inspired Framework for Hyperspectral Band Selection</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">— Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29684080" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">— Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and the power of metaheuristic algorithms to solve combinatorial optimizations. Simulations on two public data sets have shown that the proposed framework can indeed improve the effectiveness of the OPF and considerably reduce data storage costs.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29684080" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="569a5937f2cdbdf64faa76fa10a48b10" rel="nofollow" data-download="{"attachment_id":50123005,"asset_id":29684080,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50123005/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="285739" href="https://unesp.academia.edu/JoaoPauloPapa">Joao Paulo Papa</a><script data-card-contents-for-user="285739" type="text/json">{"id":285739,"first_name":"Joao","last_name":"Paulo Papa","domain_name":"unesp","page_name":"JoaoPauloPapa","display_name":"Joao Paulo Papa","profile_url":"https://unesp.academia.edu/JoaoPauloPapa?f_ri=84562","photo":"https://0.academia-photos.com/285739/2660531/3097209/s65_joao.paulo_papa.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-29684080">+1</span><div class="hidden js-additional-users-29684080"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-29684080'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-29684080').html(); 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This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and the power of metaheuristic algorithms to solve combinatorial optimizations. Simulations on two public data sets have shown that the proposed framework can indeed improve the effectiveness of the OPF and considerably reduce data storage costs.","downloadable_attachments":[{"id":50123005,"asset_id":29684080,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":285739,"first_name":"Joao","last_name":"Paulo Papa","domain_name":"unesp","page_name":"JoaoPauloPapa","display_name":"Joao Paulo Papa","profile_url":"https://unesp.academia.edu/JoaoPauloPapa?f_ri=84562","photo":"https://0.academia-photos.com/285739/2660531/3097209/s65_joao.paulo_papa.jpg"},{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562","nofollow":true},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40876851 coauthored" data-work_id="40876851" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40876851/Artificial_Soul_Optimization_An_Invention">Artificial Soul Optimization - An Invention</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">Artificial Soul Optimization -  An Invention</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40876851" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6ccc8f5fb4d934e84987869dad232ed9" rel="nofollow" data-download="{"attachment_id":61164200,"asset_id":40876851,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61164200/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="82365886" href="https://iitr-in.academia.edu/SatishGajawada">Satish Gajawada</a><script data-card-contents-for-user="82365886" type="text/json">{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-40876851">+1</span><div class="hidden js-additional-users-40876851"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a rel="nofollow" href="https://independent.academia.edu/HassanMoustafa5">Hassan M H Moustafa</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-40876851'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-40876851').html(); } } new 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u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3523" rel="nofollow" href="https://www.academia.edu/Documents/in/Evolutionary_Computation">Evolutionary Computation</a>, <script data-card-contents-for-ri="3523" type="text/json">{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5026" rel="nofollow" href="https://www.academia.edu/Documents/in/Genetic_Algorithms">Genetic Algorithms</a><script data-card-contents-for-ri="5026" type="text/json">{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=40876851]'), work: {"id":40876851,"title":"Artificial Soul Optimization - An 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Moustafa","profile_url":"https://independent.academia.edu/HassanMoustafa5?f_ri=84562","photo":"https://0.academia-photos.com/43092496/30224069/28035701/s65_hassan.moustafa.jpg"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=84562","nofollow":true},{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation?f_ri=84562","nofollow":true},{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization?f_ri=84562"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":92004,"name":"Bio Inspired Computing","url":"https://www.academia.edu/Documents/in/Bio_Inspired_Computing?f_ri=84562"},{"id":280773,"name":"Meta heuristics Algorithms","url":"https://www.academia.edu/Documents/in/Meta_heuristics_Algorithms?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":1327946,"name":"Nature inspired optimization algorithm","url":"https://www.academia.edu/Documents/in/Nature_inspired_optimization_algorithm?f_ri=84562"},{"id":2759061,"name":"Artificial Human Optimization","url":"https://www.academia.edu/Documents/in/Artificial_Human_Optimization?f_ri=84562"},{"id":3442238,"name":"Artificial Soul Optimization","url":"https://www.academia.edu/Documents/in/Artificial_Soul_Optimization?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_33506786" data-work_id="33506786" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/33506786/Global_Convergence_Analysis_of_the_Flower_Pollination_Algorithm_A_Discrete_Time_Markov_Chain_Approach">Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_33506786" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/33506786" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b3beb4fdf5fcb6d4a407f192c9084dfb" rel="nofollow" data-download="{"attachment_id":53544177,"asset_id":33506786,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53544177/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_33506786 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="33506786"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 33506786, container: ".js-paper-rank-work_33506786", }); 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The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently.","downloadable_attachments":[{"id":53544177,"asset_id":33506786,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562","nofollow":true},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15627526" data-work_id="15627526" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15627526/Nature_inspired_computing_technology_and_applications">Nature-inspired computing technology and applications</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Increasing demands upon current computer systems, along with technological changes, create a need for more flexible and adaptable systems. Natural systems provide many examples of the type of versatile system required. This paper reviews... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15627526" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Increasing demands upon current computer systems, along with technological changes, create a need for more flexible and adaptable systems. Natural systems provide many examples of the type of versatile system required. This paper reviews examples of nature-inspired computing, drawing inspiration from many different areas of living systems including evolution, ecology, development and behaviour. The implications for the future development of computing technology and applications are also discussed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15627526" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="51ddab5252a979bd0cda93d7273a660e" rel="nofollow" data-download="{"attachment_id":38745863,"asset_id":15627526,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38745863/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="20579656" href="https://ljmu.academia.edu/PaulMarrow">Paul Marrow</a><script data-card-contents-for-user="20579656" type="text/json">{"id":20579656,"first_name":"Paul","last_name":"Marrow","domain_name":"ljmu","page_name":"PaulMarrow","display_name":"Paul Marrow","profile_url":"https://ljmu.academia.edu/PaulMarrow?f_ri=84562","photo":"https://0.academia-photos.com/20579656/5687202/6470482/s65_paul.marrow.jpg"}</script></span></span></li><li class="js-paper-rank-work_15627526 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15627526"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15627526, container: ".js-paper-rank-work_15627526", }); 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$(".js-view-count[data-work-id=15627526]").text(description); $(".js-view-count-work_15627526").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_15627526").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="15627526"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="471" rel="nofollow" href="https://www.academia.edu/Documents/in/Robotics_Computer_Science_">Robotics (Computer Science)</a>, <script data-card-contents-for-ri="471" type="text/json">{"id":471,"name":"Robotics (Computer Science)","url":"https://www.academia.edu/Documents/in/Robotics_Computer_Science_?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1701" rel="nofollow" href="https://www.academia.edu/Documents/in/Evolutionary_algorithms">Evolutionary algorithms</a>, <script data-card-contents-for-ri="1701" type="text/json">{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=15627526]'), work: {"id":15627526,"title":"Nature-inspired computing technology and applications","created_at":"2015-09-12T08:01:03.525-07:00","url":"https://www.academia.edu/15627526/Nature_inspired_computing_technology_and_applications?f_ri=84562","dom_id":"work_15627526","summary":"Increasing demands upon current computer systems, along with technological changes, create a need for more flexible and adaptable systems. Natural systems provide many examples of the type of versatile system required. This paper reviews examples of nature-inspired computing, drawing inspiration from many different areas of living systems including evolution, ecology, development and behaviour. The implications for the future development of computing technology and applications are also discussed.","downloadable_attachments":[{"id":38745863,"asset_id":15627526,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":20579656,"first_name":"Paul","last_name":"Marrow","domain_name":"ljmu","page_name":"PaulMarrow","display_name":"Paul Marrow","profile_url":"https://ljmu.academia.edu/PaulMarrow?f_ri=84562","photo":"https://0.academia-photos.com/20579656/5687202/6470482/s65_paul.marrow.jpg"}],"research_interests":[{"id":471,"name":"Robotics (Computer Science)","url":"https://www.academia.edu/Documents/in/Robotics_Computer_Science_?f_ri=84562","nofollow":true},{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2639447 coauthored" data-work_id="2639447" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2639447/COMPUTING_NATURE_Gordana_Dodig_Crnkovic_and_Raffaela_Giovagnoli">COMPUTING NATURE - Gordana Dodig Crnkovic and Raffaela Giovagnoli</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This book is about nature considered as the totality of physical existence, the universe, and our present day attempts to understand it. If we see the universe as a network of networks of computational processes at many different levels... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_2639447" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This book is about nature considered as the totality of physical existence, the universe, and our present day attempts to understand it. If we see the universe as a network of networks of computational processes at many different levels of organization, what can we learn about physics, biology, cognition, social systems, and ecology expressed through interacting networks of elementary particles, atoms, molecules, cells, (and especially neurons when it comes to understanding of cognition and intelligence), organs, organisms and their ecologies? <br /> <br />Regarding our computational models of natural phenomena Feynman famously wondered: “Why should it take an infinite amount of logic to figure out what one tiny piece of space/time is going to do?” Phenomena themselves occur so quickly and automatically in nature. Can we learn how to harness nature’s computational power as we harness its energy and materials? <br /> <br />This volume includes a selection of contributions from the Symposium on Natural Computing/Unconventional Computing and Its Philosophical Significance, organized during the AISB/IACAP World Congress 2012, held in Birmingham, UK, on July 2-6, on the occasion of the centenary of Alan Turing’s birth. In this book, leading researchers investigated questions of computing nature by exploring various facets of computation as we find it in nature: relationships between different levels of computation, cognition with learning and intelligence, mathematical background, relationships to classical Turing computation and Turing’s ideas about computing nature - unorganized machines and morphogenesis. It addresses questions of information, representation and computation, interaction as communication, concurrency and agent models; in short this book presents natural computing and unconventional computing as extension of the idea of computation as symbol manipulation. <br /> <br /><a href="https://www.amazon.com/Computing-Nature-Perspective-Philosophy-Epistemology-dp-3642372244/dp/3642372244/ref=mt_hardcover?_encoding=UTF8&me=&qid=" rel="nofollow">https://www.amazon.com/Computing-Nature-Perspective-Philosophy-Epistemology-dp-3642372244/dp/3642372244/ref=mt_hardcover?_encoding=UTF8&me=&qid=</a></div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2639447" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="349d83a50a09f8612f2cb65af2072b19" rel="nofollow" data-download="{"attachment_id":61680936,"asset_id":2639447,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61680936/download_file?st=MTczOTkxNDExOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="223025" href="https://chalmers.academia.edu/GordanaDodigCrnkovic">Gordana Dodig-Crnkovic</a><script data-card-contents-for-user="223025" type="text/json">{"id":223025,"first_name":"Gordana","last_name":"Dodig-Crnkovic","domain_name":"chalmers","page_name":"GordanaDodigCrnkovic","display_name":"Gordana Dodig-Crnkovic","profile_url":"https://chalmers.academia.edu/GordanaDodigCrnkovic?f_ri=84562","photo":"https://0.academia-photos.com/223025/58617/141578684/s65_gordana.dodig-crnkovic.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-2639447">+1</span><div class="hidden js-additional-users-2639447"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://donmarcoceccarelli.academia.edu/raffaelagiovagnoli">raffaela giovagnoli</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-2639447'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-2639447').html(); 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_2639447 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="2639447"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2639447; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2639447]").text(description); $(".js-view-count-work_2639447").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_2639447").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="2639447"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a><script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=2639447]'), work: {"id":2639447,"title":"COMPUTING NATURE - Gordana Dodig Crnkovic and Raffaela Giovagnoli","created_at":"2013-02-24T03:22:42.202-08:00","url":"https://www.academia.edu/2639447/COMPUTING_NATURE_Gordana_Dodig_Crnkovic_and_Raffaela_Giovagnoli?f_ri=84562","dom_id":"work_2639447","summary":"This book is about nature considered as the totality of physical existence, the universe, and our present day attempts to understand it. If we see the universe as a network of networks of computational processes at many different levels of organization, what can we learn about physics, biology, cognition, social systems, and ecology expressed through interacting networks of elementary particles, atoms, molecules, cells, (and especially neurons when it comes to understanding of cognition and intelligence), organs, organisms and their ecologies?\r\n\r\nRegarding our computational models of natural phenomena Feynman famously wondered: “Why should it take an infinite amount of logic to figure out what one tiny piece of space/time is going to do?” Phenomena themselves occur so quickly and automatically in nature. Can we learn how to harness nature’s computational power as we harness its energy and materials?\r\n\r\nThis volume includes a selection of contributions from the Symposium on Natural Computing/Unconventional Computing and Its Philosophical Significance, organized during the AISB/IACAP World Congress 2012, held in Birmingham, UK, on July 2-6, on the occasion of the centenary of Alan Turing’s birth. In this book, leading researchers investigated questions of computing nature by exploring various facets of computation as we find it in nature: relationships between different levels of computation, cognition with learning and intelligence, mathematical background, relationships to classical Turing computation and Turing’s ideas about computing nature - unorganized machines and morphogenesis. It addresses questions of information, representation and computation, interaction as communication, concurrency and agent models; in short this book presents natural computing and unconventional computing as extension of the idea of computation as symbol manipulation.\r\n\r\nhttps://www.amazon.com/Computing-Nature-Perspective-Philosophy-Epistemology-dp-3642372244/dp/3642372244/ref=mt_hardcover?_encoding=UTF8\u0026me=\u0026qid=","downloadable_attachments":[{"id":61680936,"asset_id":2639447,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":223025,"first_name":"Gordana","last_name":"Dodig-Crnkovic","domain_name":"chalmers","page_name":"GordanaDodigCrnkovic","display_name":"Gordana Dodig-Crnkovic","profile_url":"https://chalmers.academia.edu/GordanaDodigCrnkovic?f_ri=84562","photo":"https://0.academia-photos.com/223025/58617/141578684/s65_gordana.dodig-crnkovic.jpg"},{"id":62589556,"first_name":"raffaela","last_name":"giovagnoli","domain_name":"donmarcoceccarelli","page_name":"raffaelagiovagnoli","display_name":"raffaela giovagnoli","profile_url":"https://donmarcoceccarelli.academia.edu/raffaelagiovagnoli?f_ri=84562","photo":"https://0.academia-photos.com/62589556/16864962/17653308/s65_raffaela.giovagnoli.jpg"}],"research_interests":[{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_37696674" data-work_id="37696674" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/37696674/Global_Convergence_Analysis_of_the_Bat_Algorithm_Using_a_Markovian_Framework_and_Dynamical_System_Theory">Global Convergence Analysis of the Bat Algorithm Using a Markovian Framework and Dynamical System Theory</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37696674" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria. Then, we prove that the bat algorithm can have global convergence. In addition, in order to enhance the convergence performance of the algorithm and to identify the possible effect of parameter settings on convergence, we have designed an updated model in terms of a dynamic matrix. Subsequently, we have used the stability theory of discrete-time dynamical systems to obtain the stable parameter ranges for the algorithm. Furthermore, we use some benchmark functions to demonstrate that BA can indeed achieve global optimality efficiently for these functions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37696674" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e7262287b23ddf69c3a896eceea91ba3" rel="nofollow" data-download="{"attachment_id":57687919,"asset_id":37696674,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57687919/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_37696674 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="37696674"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 37696674, container: ".js-paper-rank-work_37696674", }); 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$(".js-view-count[data-work-id=37696674]").text(description); $(".js-view-count-work_37696674").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_37696674").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="37696674"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1080" rel="nofollow" href="https://www.academia.edu/Documents/in/Dynamical_Systems">Dynamical Systems</a>, <script data-card-contents-for-ri="1080" type="text/json">{"id":1080,"name":"Dynamical Systems","url":"https://www.academia.edu/Documents/in/Dynamical_Systems?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="15124" rel="nofollow" href="https://www.academia.edu/Documents/in/Convergence">Convergence</a>, <script data-card-contents-for-ri="15124" type="text/json">{"id":15124,"name":"Convergence","url":"https://www.academia.edu/Documents/in/Convergence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="43981" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization">Optimization</a>, <script data-card-contents-for-ri="43981" type="text/json">{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a><script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=37696674]'), work: {"id":37696674,"title":"Global Convergence Analysis of the Bat Algorithm Using a Markovian Framework and Dynamical System Theory","created_at":"2018-11-03T13:24:10.079-07:00","url":"https://www.academia.edu/37696674/Global_Convergence_Analysis_of_the_Bat_Algorithm_Using_a_Markovian_Framework_and_Dynamical_System_Theory?f_ri=84562","dom_id":"work_37696674","summary":"The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria. Then, we prove that the bat algorithm can have global convergence. In addition, in order to enhance the convergence performance of the algorithm and to identify the possible effect of parameter settings on convergence, we have designed an updated model in terms of a dynamic matrix. Subsequently, we have used the stability theory of discrete-time dynamical systems to obtain the stable parameter ranges for the algorithm. Furthermore, we use some benchmark functions to demonstrate that BA can indeed achieve global optimality efficiently for these functions.","downloadable_attachments":[{"id":57687919,"asset_id":37696674,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":1080,"name":"Dynamical Systems","url":"https://www.academia.edu/Documents/in/Dynamical_Systems?f_ri=84562","nofollow":true},{"id":15124,"name":"Convergence","url":"https://www.academia.edu/Documents/in/Convergence?f_ri=84562","nofollow":true},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29687746 coauthored" data-work_id="29687746" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29687746/Random_Walks_L%C3%A9vy_Flights_Markov_Chains_and_Metaheuristic_Optimization">Random Walks, Lévy Flights, Markov Chains and Metaheuristic Optimization</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Stochastic components such as random walks have become an intrinsic part of modern metaheursitic algorithms. The efficiency of a metaheuristic algorithm may implicitly depend on the appropriate use of such randomization. In this paper, we... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29687746" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Stochastic components such as random walks have become an intrinsic part of modern metaheursitic algorithms. The efficiency of a metaheuristic algorithm may implicitly depend on the appropriate use of such randomization. In this paper, we provide some basic analysis and observations about random walks, Lévy flights, step sizes and efficiency using Markov theory. We show that the reason why Lévy flights are more efficient than Gaussian random walks, and the good performance of Eagle Strategy. Finally, we use bat algorithm to design a PID controller and have achieved equally good results as the classic Ziegler-Nichols tuning scheme.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29687746" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6d39a34efac0738d6c05049b8c8bbf47" rel="nofollow" data-download="{"attachment_id":50127343,"asset_id":29687746,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50127343/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-29687746">+1</span><div class="hidden js-additional-users-29687746"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://mdx.academia.edu/MehmetKaramanoglu">Prof Mehmet Karamanoglu</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-29687746'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-29687746').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_29687746 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29687746"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29687746, container: ".js-paper-rank-work_29687746", }); });</script></li><li class="js-percentile-work_29687746 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 29687746; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_29687746"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_29687746 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="29687746"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 29687746; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=29687746]").text(description); $(".js-view-count-work_29687746").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29687746").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29687746"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="125793" rel="nofollow" href="https://www.academia.edu/Documents/in/Random_Walks">Random Walks</a>, <script data-card-contents-for-ri="125793" type="text/json">{"id":125793,"name":"Random Walks","url":"https://www.academia.edu/Documents/in/Random_Walks?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29687746]'), work: {"id":29687746,"title":"Random Walks, Lévy Flights, Markov Chains and Metaheuristic Optimization","created_at":"2016-11-05T11:27:56.930-07:00","url":"https://www.academia.edu/29687746/Random_Walks_L%C3%A9vy_Flights_Markov_Chains_and_Metaheuristic_Optimization?f_ri=84562","dom_id":"work_29687746","summary":"Stochastic components such as random walks have become an intrinsic part of modern metaheursitic algorithms. The efficiency of a metaheuristic algorithm may implicitly depend on the appropriate use of such randomization. In this paper, we provide some basic analysis and observations about random walks, Lévy flights, step sizes and efficiency using Markov theory. We show that the reason why Lévy flights are more efficient than Gaussian random walks, and the good performance of Eagle Strategy. Finally, we use bat algorithm to design a PID controller and have achieved equally good results as the classic Ziegler-Nichols tuning scheme.","downloadable_attachments":[{"id":50127343,"asset_id":29687746,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"},{"id":17509,"first_name":"Prof Mehmet","last_name":"Karamanoglu","domain_name":"mdx","page_name":"MehmetKaramanoglu","display_name":"Prof Mehmet Karamanoglu","profile_url":"https://mdx.academia.edu/MehmetKaramanoglu?f_ri=84562","photo":"https://0.academia-photos.com/17509/5862/25669535/s65_prof_mehmet.karamanoglu.jpg"}],"research_interests":[{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":125793,"name":"Random Walks","url":"https://www.academia.edu/Documents/in/Random_Walks?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29683712" data-work_id="29683712" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29683712/Random_key_cuckoo_search_for_the_travelling_salesman_problem">Random-key cuckoo search for the travelling salesman problem</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random-key cuckoo search (RKCS) algorithm for solving the famous travelling salesman problem (TSP). We used a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29683712" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random-key cuckoo search (RKCS) algorithm for solving the famous travelling salesman problem (TSP). We used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. We also consider the displacement of a solution in both spaces using Lévy flights. The performance of the proposed RKCS is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that RKCS is superior to some other metaheuristic algorithms.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29683712" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d37250c2e0f52cd7dcded1c690451b6a" rel="nofollow" data-download="{"attachment_id":50122672,"asset_id":29683712,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50122672/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29683712 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29683712"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29683712, container: ".js-paper-rank-work_29683712", }); 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$(".js-view-count[data-work-id=29683712]").text(description); $(".js-view-count-work_29683712").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29683712").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29683712"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2507" rel="nofollow" href="https://www.academia.edu/Documents/in/Combinatorial_Optimization">Combinatorial Optimization</a>, <script data-card-contents-for-ri="2507" type="text/json">{"id":2507,"name":"Combinatorial Optimization","url":"https://www.academia.edu/Documents/in/Combinatorial_Optimization?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="131956" rel="nofollow" href="https://www.academia.edu/Documents/in/Cuckoo_Search">Cuckoo Search</a><script data-card-contents-for-ri="131956" type="text/json">{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29683712]'), work: {"id":29683712,"title":"Random-key cuckoo search for the travelling salesman problem","created_at":"2016-11-05T06:30:47.878-07:00","url":"https://www.academia.edu/29683712/Random_key_cuckoo_search_for_the_travelling_salesman_problem?f_ri=84562","dom_id":"work_29683712","summary":"Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random-key cuckoo search (RKCS) algorithm for solving the famous travelling salesman problem (TSP). We used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. We also consider the displacement of a solution in both spaces using Lévy flights. The performance of the proposed RKCS is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that RKCS is superior to some other metaheuristic algorithms.","downloadable_attachments":[{"id":50122672,"asset_id":29683712,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":2507,"name":"Combinatorial Optimization","url":"https://www.academia.edu/Documents/in/Combinatorial_Optimization?f_ri=84562","nofollow":true},{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":810821,"name":"Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Nature_Inspired_Algorithms?f_ri=84562"},{"id":1330965,"name":"Cuckoo Search Algorithm","url":"https://www.academia.edu/Documents/in/Cuckoo_Search_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36993620 coauthored" data-work_id="36993620" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36993620/HIDE_Human_Inspired_Differential_Evolution_An_Algorithm_under_Artificial_Human_Optimization_Field">HIDE : Human Inspired Differential Evolution - An Algorithm under Artificial Human Optimization Field</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Artificial Human Optimization is a new field that came into existence on December 2016. All the optimization algorithms that were created and are being created based on Artificial Humans will come under Artificial Human Optimization... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36993620" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Artificial Human Optimization is a new field that came into existence on December 2016. All the optimization algorithms that were created and are being created based on Artificial Humans will come under Artificial Human Optimization Field. Just like agents in Ant Colony Optimization are Artificial Ants, agents in Bee Colony Optimization are Artificial Bees, agents in Genetic Algorithms are Artificial chromosomes, agents in Particle Swarm Optimization are Artificial Birds or Artificial Fishes, similarly agents in Artificial Human Optimization Algorithms are Artificial Humans. “Multiple Strategy Human Optimization (MSHO)” is a new algorithm designed recently based on Artificial Humans. The key concept in MSHO is to use more than one strategy in the optimization process. Two strategies are used in MSHO. One strategy is to move towards the best individual in one generation. Another strategy is to move away from the worst individual in next generation. Differential Evolution is a popular algorithm for solving optimization problems in various domains. In this paper “Human Inspired Differential Evolution (HIDE)” is proposed. The idea of HIDE algorithm is to use the concept of Multiple Strategies of MSHO algorithm in Differential Evolution. The mutation operator of Differential Evolution algorithm is modified to incorporate the key concept of MSHO algorithm in Differential Evolution. The proposed HIDE algorithm is tested by applying it on a complex benchmark problem.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36993620" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c5c4533e9c30510f5c0c816f8d757e0f" rel="nofollow" data-download="{"attachment_id":56942697,"asset_id":36993620,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56942697/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="82365886" href="https://iitr-in.academia.edu/SatishGajawada">Satish Gajawada</a><script data-card-contents-for-user="82365886" type="text/json">{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-36993620">+1</span><div class="hidden js-additional-users-36993620"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a rel="nofollow" href="https://independent.academia.edu/HassanMoustafa5">Hassan M H Moustafa</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-36993620'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-36993620').html(); 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_36993620 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="36993620"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36993620; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36993620]").text(description); $(".js-view-count-work_36993620").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36993620").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36993620"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">15</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5026" rel="nofollow" href="https://www.academia.edu/Documents/in/Genetic_Algorithms">Genetic Algorithms</a>, <script data-card-contents-for-ri="5026" type="text/json">{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6420" rel="nofollow" href="https://www.academia.edu/Documents/in/Ant_Colony_Optimization">Ant Colony Optimization</a><script data-card-contents-for-ri="6420" type="text/json">{"id":6420,"name":"Ant Colony Optimization","url":"https://www.academia.edu/Documents/in/Ant_Colony_Optimization?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36993620]'), work: {"id":36993620,"title":"HIDE : Human Inspired Differential Evolution - An Algorithm under Artificial Human Optimization Field","created_at":"2018-07-06T07:39:18.230-07:00","url":"https://www.academia.edu/36993620/HIDE_Human_Inspired_Differential_Evolution_An_Algorithm_under_Artificial_Human_Optimization_Field?f_ri=84562","dom_id":"work_36993620","summary":"Artificial Human Optimization is a new field that came into existence on December 2016. All the optimization algorithms that were created and are being created based on Artificial Humans will come under Artificial Human Optimization Field. Just like agents in Ant Colony Optimization are Artificial Ants, agents in Bee Colony Optimization are Artificial Bees, agents in Genetic Algorithms are Artificial chromosomes, agents in Particle Swarm Optimization are Artificial Birds or Artificial Fishes, similarly agents in Artificial Human Optimization Algorithms are Artificial Humans. “Multiple Strategy Human Optimization (MSHO)” is a new algorithm designed recently based on Artificial Humans. The key concept in MSHO is to use more than one strategy in the optimization process. Two strategies are used in MSHO. One strategy is to move towards the best individual in one generation. Another strategy is to move away from the worst individual in next generation. Differential Evolution is a popular algorithm for solving optimization problems in various domains. In this paper “Human Inspired Differential Evolution (HIDE)” is proposed. The idea of HIDE algorithm is to use the concept of Multiple Strategies of MSHO algorithm in Differential Evolution. The mutation operator of Differential Evolution algorithm is modified to incorporate the key concept of MSHO algorithm in Differential Evolution. The proposed HIDE algorithm is tested by applying it on a complex benchmark problem.","downloadable_attachments":[{"id":56942697,"asset_id":36993620,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"},{"id":43092496,"first_name":"Hassan","last_name":"Moustafa","domain_name":"independent","page_name":"HassanMoustafa5","display_name":"Hassan M H Moustafa","profile_url":"https://independent.academia.edu/HassanMoustafa5?f_ri=84562","photo":"https://0.academia-photos.com/43092496/30224069/28035701/s65_hassan.moustafa.jpg"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=84562","nofollow":true},{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true},{"id":6420,"name":"Ant Colony Optimization","url":"https://www.academia.edu/Documents/in/Ant_Colony_Optimization?f_ri=84562","nofollow":true},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution?f_ri=84562"},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization?f_ri=84562"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":92004,"name":"Bio Inspired Computing","url":"https://www.academia.edu/Documents/in/Bio_Inspired_Computing?f_ri=84562"},{"id":154208,"name":"Bio-Inspired Computing","url":"https://www.academia.edu/Documents/in/Bio-Inspired_Computing?f_ri=84562"},{"id":252813,"name":"Evolutionary Computing","url":"https://www.academia.edu/Documents/in/Evolutionary_Computing?f_ri=84562"},{"id":398158,"name":"Nature Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature_Inspired_Computing?f_ri=84562"},{"id":1452940,"name":"Artificial Bee Colony Optimization","url":"https://www.academia.edu/Documents/in/Artificial_Bee_Colony_Optimization?f_ri=84562"},{"id":2759061,"name":"Artificial Human Optimization","url":"https://www.academia.edu/Documents/in/Artificial_Human_Optimization?f_ri=84562"},{"id":2985470,"name":"Artificial Humans","url":"https://www.academia.edu/Documents/in/Artificial_Humans?f_ri=84562"},{"id":2985471,"name":"Global Optimization Techniques","url":"https://www.academia.edu/Documents/in/Global_Optimization_Techniques?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_34956581" data-work_id="34956581" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/34956581/Global_Convergence_Analysis_of_the_Flower_Pollination_Algorithm_A_Discrete_Time_Markov_Chain_Approach">Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34956581" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34956581" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="63ba48eaf342ba614e470df55fddf80a" rel="nofollow" data-download="{"attachment_id":54818795,"asset_id":34956581,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54818795/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_34956581 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34956581"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34956581, container: ".js-paper-rank-work_34956581", }); });</script></li><li class="js-percentile-work_34956581 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 34956581; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_34956581"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_34956581 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="34956581"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 34956581; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=34956581]").text(description); $(".js-view-count-work_34956581").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_34956581").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="34956581"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5395" rel="nofollow" href="https://www.academia.edu/Documents/in/Swarm_Intelligence">Swarm Intelligence</a>, <script data-card-contents-for-ri="5395" type="text/json">{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=34956581]'), work: {"id":34956581,"title":"Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach","created_at":"2017-10-25T09:16:49.275-07:00","url":"https://www.academia.edu/34956581/Global_Convergence_Analysis_of_the_Flower_Pollination_Algorithm_A_Discrete_Time_Markov_Chain_Approach?f_ri=84562","dom_id":"work_34956581","summary":"Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently.","downloadable_attachments":[{"id":54818795,"asset_id":34956581,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence?f_ri=84562","nofollow":true},{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29687675" data-work_id="29687675" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29687675/Synthesizing_Cross_Ambiguity_Functions_Using_the_Improved_Bat_Algorithm">Synthesizing Cross-Ambiguity Functions Using the Improved Bat Algorithm</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The cross-ambiguity function (CAF) relates to the correlation processing of signals in radar, sonar, and communication systems in the presence of delays and Doppler shifts. It is a commonly used tool in the analysis of signals in these... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29687675" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The cross-ambiguity function (CAF) relates to the correlation processing of signals in radar, sonar, and communication systems in the presence of delays and Doppler shifts. It is a commonly used tool in the analysis of signals in these systems when both delay and Doppler shifts are present. In this chapter, we aim to tackle the CAF synthesization problem such that the synthesized CAF approximates a desired CAF. A CAF synthesization problem is addressed by jointly designing a pair of waveforms using a metaheuristic approach based on the echolocation of bats. Through four examples, it is shown that such an approach can be used as an effective tool in synthesizing different types of CAFs.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29687675" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2b9045afca5bdbf01a1be5add19c4691" rel="nofollow" data-download="{"attachment_id":50127276,"asset_id":29687675,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50127276/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29687675 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29687675"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29687675, container: ".js-paper-rank-work_29687675", }); 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It is a commonly used tool in the analysis of signals in these systems when both delay and Doppler shifts are present. In this chapter, we aim to tackle the CAF synthesization problem such that the synthesized CAF approximates a desired CAF. A CAF synthesization problem is addressed by jointly designing a pair of waveforms using a metaheuristic approach based on the echolocation of bats. Through four examples, it is shown that such an approach can be used as an effective tool in synthesizing different types of CAFs.","downloadable_attachments":[{"id":50127276,"asset_id":29687675,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29684252" data-work_id="29684252" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29684252/New_directional_bat_algorithm_for_continuous_optimization_problems">New directional bat algorithm for continuous optimization problems</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29684252" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. In addition to such directional echolocation, three other improvements have been embedded into the standard bat algorithm to enhance its performance. The new proposed approach, namely the directional Bat Algorithm (dBA), has been then tested using several standard and non-standard benchmarks from the CEC'2005 benchmark suite. The performance of dBA has been compared with ten other algorithms and BA variants using non-parametric statistical tests. The statistical test results show the superiority of the directional bat algorithm.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29684252" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="686d14f0c1bb78aeb5b8bb574488d2ec" rel="nofollow" data-download="{"attachment_id":50123025,"asset_id":29684252,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50123025/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29684252 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29684252"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29684252, container: ".js-paper-rank-work_29684252", }); 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$(".js-view-count[data-work-id=29684252]").text(description); $(".js-view-count-work_29684252").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29684252").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29684252"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="131957" rel="nofollow" href="https://www.academia.edu/Documents/in/Bat_Algorithm">Bat Algorithm</a>, <script data-card-contents-for-ri="131957" type="text/json">{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29684252]'), work: {"id":29684252,"title":"New directional bat algorithm for continuous optimization problems","created_at":"2016-11-05T06:58:21.811-07:00","url":"https://www.academia.edu/29684252/New_directional_bat_algorithm_for_continuous_optimization_problems?f_ri=84562","dom_id":"work_29684252","summary":"Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. In addition to such directional echolocation, three other improvements have been embedded into the standard bat algorithm to enhance its performance. The new proposed approach, namely the directional Bat Algorithm (dBA), has been then tested using several standard and non-standard benchmarks from the CEC'2005 benchmark suite. The performance of dBA has been compared with ten other algorithms and BA variants using non-parametric statistical tests. The statistical test results show the superiority of the directional bat algorithm.","downloadable_attachments":[{"id":50123025,"asset_id":29684252,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":810821,"name":"Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Nature_Inspired_Algorithms?f_ri=84562"},{"id":1491866,"name":"Hybrid bat algorithm","url":"https://www.academia.edu/Documents/in/Hybrid_bat_algorithm?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_44120013 coauthored" data-work_id="44120013" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/44120013/Nature_Plus_Plus_Inspired_Computing_The_Superset_of_Nature_Inspired_Computing">Nature Plus Plus Inspired Computing - The Superset of Nature Inspired Computing</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The term "Nature Plus Plus Inspired Computing" is coined by us in this article. The abbreviation for this new term is "N++IC." Just like the C++ programming language is a superset of C programming language, Nature Plus Plus Inspired... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_44120013" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The term "Nature Plus Plus Inspired Computing" is coined by us in this article. The abbreviation for this new term is "N++IC." Just like the C++ programming language is a superset of C programming language, Nature Plus Plus Inspired Computing (N++IC) field is a superset of the Nature Inspired Computing (NIC) field. We defined and introduced "Nature Plus Plus Inspired Computing Field" in this work. Several interesting opportunities in N++IC Field are shown for Artificial Intelligence Field Scientists and Students. We show a literature review of the N++IC Field after showing the definition of Nature Inspired Computing (NIC) Field. The primary purpose of publishing this innovative article is to show a new path to NIC Field Scientists so that they can come up with various innovative algorithms from scratch. As the focus of this article is to introduce N++IC to researchers across the globe, we added N++IC Field concepts to the Particle Swarm Optimization algorithm and created the "Children Cycle Riding Algorithm (CCR Algorithm)." Finally, results obtained by CCR Algorithm are shown, followed by Conclusions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44120013" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8bbc156e93c359334b40dab998ef5083" rel="nofollow" data-download="{"attachment_id":64470051,"asset_id":44120013,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64470051/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="82365886" href="https://iitr-in.academia.edu/SatishGajawada">Satish Gajawada</a><script data-card-contents-for-user="82365886" type="text/json">{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-44120013">+1</span><div class="hidden js-additional-users-44120013"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a rel="nofollow" href="https://independent.academia.edu/HassanMoustafa5">Hassan M H Moustafa</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-44120013'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-44120013').html(); 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_44120013 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="44120013"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44120013; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44120013]").text(description); $(".js-view-count-work_44120013").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_44120013").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="44120013"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3521" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Intelligence">Computational Intelligence</a>, <script data-card-contents-for-ri="3521" type="text/json">{"id":3521,"name":"Computational Intelligence","url":"https://www.academia.edu/Documents/in/Computational_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="54533" rel="nofollow" href="https://www.academia.edu/Documents/in/Children">Children</a>, <script data-card-contents-for-ri="54533" type="text/json">{"id":54533,"name":"Children","url":"https://www.academia.edu/Documents/in/Children?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a><script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=44120013]'), work: {"id":44120013,"title":"Nature Plus Plus Inspired Computing - The Superset of Nature Inspired Computing","created_at":"2020-09-19T05:10:49.281-07:00","url":"https://www.academia.edu/44120013/Nature_Plus_Plus_Inspired_Computing_The_Superset_of_Nature_Inspired_Computing?f_ri=84562","dom_id":"work_44120013","summary":"The term \"Nature Plus Plus Inspired Computing\" is coined by us in this article. The abbreviation for this new term is \"N++IC.\" Just like the C++ programming language is a superset of C programming language, Nature Plus Plus Inspired Computing (N++IC) field is a superset of the Nature Inspired Computing (NIC) field. We defined and introduced \"Nature Plus Plus Inspired Computing Field\" in this work. Several interesting opportunities in N++IC Field are shown for Artificial Intelligence Field Scientists and Students. We show a literature review of the N++IC Field after showing the definition of Nature Inspired Computing (NIC) Field. The primary purpose of publishing this innovative article is to show a new path to NIC Field Scientists so that they can come up with various innovative algorithms from scratch. As the focus of this article is to introduce N++IC to researchers across the globe, we added N++IC Field concepts to the Particle Swarm Optimization algorithm and created the \"Children Cycle Riding Algorithm (CCR Algorithm).\" Finally, results obtained by CCR Algorithm are shown, followed by Conclusions.","downloadable_attachments":[{"id":64470051,"asset_id":44120013,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"},{"id":43092496,"first_name":"Hassan","last_name":"Moustafa","domain_name":"independent","page_name":"HassanMoustafa5","display_name":"Hassan M H Moustafa","profile_url":"https://independent.academia.edu/HassanMoustafa5?f_ri=84562","photo":"https://0.academia-photos.com/43092496/30224069/28035701/s65_hassan.moustafa.jpg"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true},{"id":3521,"name":"Computational Intelligence","url":"https://www.academia.edu/Documents/in/Computational_Intelligence?f_ri=84562","nofollow":true},{"id":54533,"name":"Children","url":"https://www.academia.edu/Documents/in/Children?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":252813,"name":"Evolutionary Computing","url":"https://www.academia.edu/Documents/in/Evolutionary_Computing?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42391482" data-work_id="42391482" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42391482/Multiobjective_Cuckoo_Search_MOCS_">Multiobjective Cuckoo Search (MOCS)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The multiobjective cuckoo search (MOCS) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42391482" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The multiobjective cuckoo search (MOCS) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, dimensionality, various parameters, and simple lower and upper bounds (Lb, Ub).<br /><br />Yang, Xin-She, and Suash Deb. “Multiobjective Cuckoo Search for Design Optimization.” Computers & Operations Research, vol. 40, no. 6, Elsevier BV, June 2013, pp. 1616–24, doi:10.1016/j.cor.2011.09.026.<br /><br />[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42391482" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="830b805bd4eb93e777f3866f59f83de8" rel="nofollow" data-download="{"attachment_id":62554274,"asset_id":42391482,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62554274/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_42391482 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42391482"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42391482, container: ".js-paper-rank-work_42391482", }); });</script></li><li class="js-percentile-work_42391482 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42391482; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_42391482"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_42391482 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="42391482"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42391482; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=42391482]").text(description); $(".js-view-count-work_42391482").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42391482").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42391482"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="13445" rel="nofollow" href="https://www.academia.edu/Documents/in/Multiobjective_Optimization">Multiobjective Optimization</a>, <script data-card-contents-for-ri="13445" type="text/json">{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="131956" rel="nofollow" href="https://www.academia.edu/Documents/in/Cuckoo_Search">Cuckoo Search</a>, <script data-card-contents-for-ri="131956" type="text/json">{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="278933" rel="nofollow" href="https://www.academia.edu/Documents/in/X._S._Yang_Nature-Inspired_Metaheuristic_Algorithms">X. S. Yang, Nature-Inspired Metaheuristic Algorithms</a><script data-card-contents-for-ri="278933" type="text/json">{"id":278933,"name":"X. S. Yang, Nature-Inspired Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/X._S._Yang_Nature-Inspired_Metaheuristic_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42391482]'), work: {"id":42391482,"title":"Multiobjective Cuckoo Search (MOCS)","created_at":"2020-03-30T07:12:04.066-07:00","url":"https://www.academia.edu/42391482/Multiobjective_Cuckoo_Search_MOCS_?f_ri=84562","dom_id":"work_42391482","summary":"The multiobjective cuckoo search (MOCS) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, dimensionality, various parameters, and simple lower and upper bounds (Lb, Ub).\n\nYang, Xin-She, and Suash Deb. “Multiobjective Cuckoo Search for Design Optimization.” Computers \u0026 Operations Research, vol. 40, no. 6, Elsevier BV, June 2013, pp. 1616–24, doi:10.1016/j.cor.2011.09.026.\n\n[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]","downloadable_attachments":[{"id":62554274,"asset_id":42391482,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search?f_ri=84562","nofollow":true},{"id":278933,"name":"X. S. Yang, Nature-Inspired Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/X._S._Yang_Nature-Inspired_Metaheuristic_Algorithms?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":1309722,"name":"Cuckoo Search Algorithm for Optimization of Multiobjective Function","url":"https://www.academia.edu/Documents/in/Cuckoo_Search_Algorithm_for_Optimization_of_Multiobjective_Function?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29684307" data-work_id="29684307" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29684307/A_Discrete_Firefly_Algorithm_to_Solve_a_Rich_Vehicle_Routing_Problem_Modelling_a_Newspaper_Distribution_System_with_Recycling_Policy">A Discrete Firefly Algorithm to Solve a Rich Vehicle Routing Problem Modelling a Newspaper Distribution System with Recycling Policy</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29684307" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29684307" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e6414e13a8726f05e7c4de4e3d4ec359" rel="nofollow" data-download="{"attachment_id":50123122,"asset_id":29684307,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50123122/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a><script data-card-contents-for-user="344652" type="text/json">{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_29684307 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29684307"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29684307, container: ".js-paper-rank-work_29684307", }); 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$(".js-view-count[data-work-id=29684307]").text(description); $(".js-view-count-work_29684307").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29684307").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29684307"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="10924" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization_techniques">Optimization techniques</a>, <script data-card-contents-for-ri="10924" type="text/json">{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="60471" rel="nofollow" href="https://www.academia.edu/Documents/in/Vehicle_Routing_Problems">Vehicle Routing Problems</a>, <script data-card-contents-for-ri="60471" type="text/json">{"id":60471,"name":"Vehicle Routing Problems","url":"https://www.academia.edu/Documents/in/Vehicle_Routing_Problems?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="176826" rel="nofollow" href="https://www.academia.edu/Documents/in/Firefly_Algorithm">Firefly Algorithm</a><script data-card-contents-for-ri="176826" type="text/json">{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29684307]'), work: {"id":29684307,"title":"A Discrete Firefly Algorithm to Solve a Rich Vehicle Routing Problem Modelling a Newspaper Distribution System with Recycling Policy","created_at":"2016-11-05T07:00:23.567-07:00","url":"https://www.academia.edu/29684307/A_Discrete_Firefly_Algorithm_to_Solve_a_Rich_Vehicle_Routing_Problem_Modelling_a_Newspaper_Distribution_System_with_Recycling_Policy?f_ri=84562","dom_id":"work_29684307","summary":"A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics.","downloadable_attachments":[{"id":50123122,"asset_id":29684307,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques?f_ri=84562","nofollow":true},{"id":60471,"name":"Vehicle Routing Problems","url":"https://www.academia.edu/Documents/in/Vehicle_Routing_Problems?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":176826,"name":"Firefly Algorithm","url":"https://www.academia.edu/Documents/in/Firefly_Algorithm?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_23883453" data-work_id="23883453" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/23883453/Nature_Inspired_Computation_in_Engineering">Nature-Inspired Computation in Engineering</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">A recent book on Nature-Inspired Computation with applications in Engineering.</div></div><ul class="InlineList u-ph0x u-fs13"><li 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href="https://www.academia.edu/Documents/in/Evolutionary_algorithms">Evolutionary algorithms</a>, <script data-card-contents-for-ri="1701" type="text/json">{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="43981" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimization">Optimization</a>, <script data-card-contents-for-ri="43981" type="text/json">{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired 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Engineering.","downloadable_attachments":[{"id":44272854,"asset_id":23883453,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms?f_ri=84562","nofollow":true},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42169205" data-work_id="42169205" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42169205/Open_Problems_and_Analysis_of_Nature_Inspired_Optimization_Algorithms_LION2019_Keynote_">Open Problems and Analysis of Nature-Inspired Optimization Algorithms (LION2019 Keynote)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">These slides are the keynote talk by Xin-She Yang at LION2019 Learning and Intelligent Optimization Conference (Crete, Greece) .</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42169205" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d9bd4b12ed723dc73a2240f1a827a2d3" rel="nofollow" data-download="{"attachment_id":62309347,"asset_id":42169205,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" 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style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42169205"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1701" rel="nofollow" href="https://www.academia.edu/Documents/in/Evolutionary_algorithms">Evolutionary algorithms</a>, <script data-card-contents-for-ri="1701" type="text/json">{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84562" rel="nofollow" href="https://www.academia.edu/Documents/in/Nature-Inspired_Computing">Nature-Inspired Computing</a>, <script data-card-contents-for-ri="84562" type="text/json">{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="86588" rel="nofollow" href="https://www.academia.edu/Documents/in/Metaheuristic_Algorithms">Metaheuristic Algorithms</a>, <script data-card-contents-for-ri="86588" type="text/json">{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="423243" rel="nofollow" href="https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms">Bio and Nature Inspired Algorithms</a><script data-card-contents-for-ri="423243" type="text/json">{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42169205]'), work: {"id":42169205,"title":"Open Problems and Analysis of Nature-Inspired Optimization Algorithms (LION2019 Keynote)","created_at":"2020-03-08T10:10:25.958-07:00","url":"https://www.academia.edu/42169205/Open_Problems_and_Analysis_of_Nature_Inspired_Optimization_Algorithms_LION2019_Keynote_?f_ri=84562","dom_id":"work_42169205","summary":"These slides are the keynote talk by Xin-She Yang at LION2019 Learning and Intelligent Optimization Conference (Crete, Greece) .","downloadable_attachments":[{"id":62309347,"asset_id":42169205,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":344652,"first_name":"Xin-She","last_name":"Yang","domain_name":"cambridge","page_name":"XinSheYang","display_name":"Xin-She Yang","profile_url":"https://cambridge.academia.edu/XinSheYang?f_ri=84562","photo":"https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg"}],"research_interests":[{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true},{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms?f_ri=84562","nofollow":true},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562","nofollow":true},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm?f_ri=84562"},{"id":583837,"name":"Convergence Analysis","url":"https://www.academia.edu/Documents/in/Convergence_Analysis?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_41731484" data-work_id="41731484" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/41731484/Algorithms_for_Reorganizing_Branches_within_Enterprise_Network">Algorithms for Reorganizing Branches within Enterprise Network</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The problem of reorganizing branches in an enterprise network is based on a weighted graph problem formulation. The suboptimal solution to this problem is obtained by applying a two-phase algorithm. The first is to decompose the graph... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_41731484" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The problem of reorganizing branches in an enterprise network is based on a weighted graph problem formulation. The suboptimal solution to this problem is obtained by applying a two-phase algorithm. The first is to decompose the graph into different sections in such a way that those sections are equally balanced. The second phase is to find a service centre for each section. In this paper, we propose an improvement of a hybrid genetic algorithm for decomposing the graph into different sections. We also propose new algorithms for finding centres of sections and we compare them on an illustrated examples.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/41731484" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8e1550020531ebf2eed295ca70195bae" rel="nofollow" data-download="{"attachment_id":61889330,"asset_id":41731484,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61889330/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="35940817" href="https://hct.academia.edu/MilanDjordjevic">Milan Dordevic</a><script data-card-contents-for-user="35940817" type="text/json">{"id":35940817,"first_name":"Milan","last_name":"Dordevic","domain_name":"hct","page_name":"MilanDjordjevic","display_name":"Milan Dordevic","profile_url":"https://hct.academia.edu/MilanDjordjevic?f_ri=84562","photo":"https://0.academia-photos.com/35940817/10386753/71184138/s65_milan.djordjevic.jpg"}</script></span></span></li><li class="js-paper-rank-work_41731484 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="41731484"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 41731484, container: ".js-paper-rank-work_41731484", }); 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The suboptimal solution to this problem is obtained by applying a two-phase algorithm. The first is to decompose the graph into different sections in such a way that those sections are equally balanced. The second phase is to find a service centre for each section. In this paper, we propose an improvement of a hybrid genetic algorithm for decomposing the graph into different sections. We also propose new algorithms for finding centres of sections and we compare them on an illustrated examples.","downloadable_attachments":[{"id":61889330,"asset_id":41731484,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35940817,"first_name":"Milan","last_name":"Dordevic","domain_name":"hct","page_name":"MilanDjordjevic","display_name":"Milan Dordevic","profile_url":"https://hct.academia.edu/MilanDjordjevic?f_ri=84562","photo":"https://0.academia-photos.com/35940817/10386753/71184138/s65_milan.djordjevic.jpg"}],"research_interests":[{"id":5436,"name":"Combinatorics","url":"https://www.academia.edu/Documents/in/Combinatorics?f_ri=84562","nofollow":true},{"id":25773,"name":"Operations research and Optimization","url":"https://www.academia.edu/Documents/in/Operations_research_and_Optimization?f_ri=84562","nofollow":true},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36758476" data-work_id="36758476" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36758476/An_Ocean_of_Opportunities_in_Artificial_Human_Optimization_Field">An Ocean of Opportunities in Artificial Human Optimization Field</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Global Optimization Techniques like Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization and other optimization techniques were used in literature to solve complex optimization problems. Many optimization algorithms... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36758476" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Global Optimization Techniques like Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization and other optimization techniques were used in literature to solve complex optimization problems. Many optimization algorithms were proposed in literature by taking the behavior of Birds, Ants, Fishes, Chromosomes etc. as inspiration. Recently, a new trend has begun in Evolutionary Computing Domain where optimization algorithms have been created by taking Human Behavior as inspiration. The focus of this paper is on optimization algorithms that were and are being created based on the behavior of Artificial Humans. In December 2016, a new field titled " Artificial Human Optimization " was proposed in literature. This paper is strongly meant to popularize " Artificial Human Optimization " field like never before by showing an Ocean of Opportunities that exists in this new and interesting area of research. A new field titled " Artificial Economics Optimization " is proposed at the end of paper.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36758476" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="dbbd5c2a0caf9ba2fc45e8b6a954e6e4" rel="nofollow" data-download="{"attachment_id":56704912,"asset_id":36758476,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56704912/download_file?st=MTczOTkxNDEyMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="82365886" href="https://iitr-in.academia.edu/SatishGajawada">Satish Gajawada</a><script data-card-contents-for-user="82365886" type="text/json">{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"}</script></span></span></li><li class="js-paper-rank-work_36758476 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36758476"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36758476, container: ".js-paper-rank-work_36758476", }); 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$(".js-view-count[data-work-id=36758476]").text(description); $(".js-view-count-work_36758476").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36758476").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36758476"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">14</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5026" rel="nofollow" href="https://www.academia.edu/Documents/in/Genetic_Algorithms">Genetic Algorithms</a>, <script data-card-contents-for-ri="5026" type="text/json">{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6420" rel="nofollow" href="https://www.academia.edu/Documents/in/Ant_Colony_Optimization">Ant Colony Optimization</a><script data-card-contents-for-ri="6420" type="text/json">{"id":6420,"name":"Ant Colony Optimization","url":"https://www.academia.edu/Documents/in/Ant_Colony_Optimization?f_ri=84562","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36758476]'), work: {"id":36758476,"title":"An Ocean of Opportunities in Artificial Human Optimization Field","created_at":"2018-06-01T03:10:50.405-07:00","url":"https://www.academia.edu/36758476/An_Ocean_of_Opportunities_in_Artificial_Human_Optimization_Field?f_ri=84562","dom_id":"work_36758476","summary":"Global Optimization Techniques like Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization and other optimization techniques were used in literature to solve complex optimization problems. Many optimization algorithms were proposed in literature by taking the behavior of Birds, Ants, Fishes, Chromosomes etc. as inspiration. Recently, a new trend has begun in Evolutionary Computing Domain where optimization algorithms have been created by taking Human Behavior as inspiration. The focus of this paper is on optimization algorithms that were and are being created based on the behavior of Artificial Humans. In December 2016, a new field titled \" Artificial Human Optimization \" was proposed in literature. This paper is strongly meant to popularize \" Artificial Human Optimization \" field like never before by showing an Ocean of Opportunities that exists in this new and interesting area of research. A new field titled \" Artificial Economics Optimization \" is proposed at the end of paper.","downloadable_attachments":[{"id":56704912,"asset_id":36758476,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":82365886,"first_name":"Satish","last_name":"Gajawada","domain_name":"iitr-in","page_name":"SatishGajawada","display_name":"Satish Gajawada","profile_url":"https://iitr-in.academia.edu/SatishGajawada?f_ri=84562","photo":"https://0.academia-photos.com/82365886/33038444/29608330/s65_satish.gajawada.jpg"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=84562","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=84562","nofollow":true},{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=84562","nofollow":true},{"id":6420,"name":"Ant Colony Optimization","url":"https://www.academia.edu/Documents/in/Ant_Colony_Optimization?f_ri=84562","nofollow":true},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution?f_ri=84562"},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization?f_ri=84562"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing?f_ri=84562"},{"id":112657,"name":"Artificial Bee Colony Algorithm","url":"https://www.academia.edu/Documents/in/Artificial_Bee_Colony_Algorithm?f_ri=84562"},{"id":154208,"name":"Bio-Inspired Computing","url":"https://www.academia.edu/Documents/in/Bio-Inspired_Computing?f_ri=84562"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms?f_ri=84562"},{"id":1389134,"name":"Bacterial Foraging Optimization Algorithm","url":"https://www.academia.edu/Documents/in/Bacterial_Foraging_Optimization_Algorithm?f_ri=84562"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm?f_ri=84562"},{"id":1956464,"name":"Artificial Fish Swarm Algorithm","url":"https://www.academia.edu/Documents/in/Artificial_Fish_Swarm_Algorithm?f_ri=84562"},{"id":2759061,"name":"Artificial Human Optimization","url":"https://www.academia.edu/Documents/in/Artificial_Human_Optimization?f_ri=84562"}]}, }) } })();</script></ul></li></ul></div></div></div><div class="u-taCenter Pagination"><ul class="pagination"><li class="next_page"><a href="/Documents/in/Nature-Inspired_Computing?after=50%2C36758476" rel="next">Next</a></li><li class="last next"><a href="/Documents/in/Nature-Inspired_Computing?page=last">Last »</a></li></ul></div></div><div class="hidden-xs hidden-sm"><div class="u-pl6x"><div style="width: 300px;"><div class="panel panel-flat u-mt7x"><div 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