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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="42168390"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/42168390/Navigation_routing_and_nature_inspired_optimization_in_Nature_Inspired_Computation_in_Navigation_and_Routing_Problems"><img alt="Research paper thumbnail of Navigation, routing and nature-inspired optimization, in: Nature-Inspired Computation in Navigation and Routing Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/62308625/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" 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 class="wp-workCard_item"><span>Nature-Inspired Computation in Navigation and Routing Problems</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Navigation abilities are crucial for survival in nature, and there are a wide range of sophistica...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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. 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href="https://www.academia.edu/39928987/Mathematical_Foundations_of_Nature_Inspired_Algorithms"><img alt="Research paper thumbnail of Mathematical Foundations of Nature-Inspired Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/60117067/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/39928987/Mathematical_Foundations_of_Nature_Inspired_Algorithms">Mathematical Foundations of Nature-Inspired Algorithms</a></div><div class="wp-workCard_item"><span>Springer Nature</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7ca705c26e7e1f0810ca73584df0677d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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href="https://www.academia.edu/39928965/Introduction_to_Algorithms_for_Data_Mining_and_Machine_Learning"><img alt="Research paper thumbnail of Introduction to Algorithms for Data Mining and Machine Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/60117048/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/39928965/Introduction_to_Algorithms_for_Data_Mining_and_Machine_Learning">Introduction to Algorithms for Data Mining and Machine Learning</a></div><div class="wp-workCard_item"><span>Academic Press/Elsevier</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Introduction to Algorithms for Data Mining and Machine Learning (book) introduces the essential ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Introduction to Algorithms for Data Mining and Machine Learning (book)  introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="28641599"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/28641599/Bio_Inspired_Computation_and_Applications_in_Image_Processing"><img alt="Research paper thumbnail of Bio-Inspired Computation and Applications in Image Processing" class="work-thumbnail" src="https://attachments.academia-assets.com/49010748/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/28641599/Bio_Inspired_Computation_and_Applications_in_Image_Processing">Bio-Inspired Computation and Applications in Image Processing</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://unesp.academia.edu/JoaoPauloPapa">Joao Paulo Papa</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A sample chapter of the Book on "Bio-inspired Computation and Applications in Image Processing" ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A sample chapter of the Book on <br />"Bio-inspired Computation and Applications in Image Processing"<br />(Elsevier, 2016).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e3a978566edf41c24ad4dc5e6c8aace1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":49010748,"asset_id":28641599,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/49010748/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="28641599"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="28641599"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 28641599; 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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. 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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. 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href="https://www.academia.edu/39928965/Introduction_to_Algorithms_for_Data_Mining_and_Machine_Learning"><img alt="Research paper thumbnail of Introduction to Algorithms for Data Mining and Machine Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/60117048/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/39928965/Introduction_to_Algorithms_for_Data_Mining_and_Machine_Learning">Introduction to Algorithms for Data Mining and Machine Learning</a></div><div class="wp-workCard_item"><span>Academic Press/Elsevier</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Introduction to Algorithms for Data Mining and Machine Learning (book) introduces the essential ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Introduction to Algorithms for Data Mining and Machine Learning (book)  introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5d2d72bf18ebd21ab98ee8fabdbf14ca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60117048,"asset_id":39928965,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60117048/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="39928965"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="39928965"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 39928965; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=39928965]").text(description); $(".js-view-count[data-work-id=39928965]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 39928965; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='39928965']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 39928965, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5d2d72bf18ebd21ab98ee8fabdbf14ca" } } $('.js-work-strip[data-work-id=39928965]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":39928965,"title":"Introduction to Algorithms for Data Mining and Machine Learning","translated_title":"","metadata":{"doi":"10.1016/B978-0-12-817216-2.00010-7","abstract":"Introduction to Algorithms for Data Mining and Machine Learning (book) introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="50388" id="papers"><div class="js-work-strip profile--work_container" data-work-id="121710677"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/121710677/A_Generalized_Evolutionary_Metaheuristic_GEM_Algorithm_for_Engineering_Optimization"><img alt="Research paper thumbnail of A Generalized Evolutionary Metaheuristic (GEM) Algorithm for Engineering Optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/116526703/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121710677/A_Generalized_Evolutionary_Metaheuristic_GEM_Algorithm_for_Engineering_Optimization">A Generalized Evolutionary Metaheuristic (GEM) Algorithm for Engineering Optimization</a></div><div class="wp-workCard_item"><span>Cogent Engineering</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Many optimization problems in engineering and industrial design applications can be formulated as...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6ebfba28cea76e5e971376dce5e32d84" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116526703,"asset_id":121710677,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116526703/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="121710677"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="121710677"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121710677; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121710677]").text(description); $(".js-view-count[data-work-id=121710677]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121710677; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121710677']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 121710677, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6ebfba28cea76e5e971376dce5e32d84" } } $('.js-work-strip[data-work-id=121710677]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121710677,"title":"A Generalized Evolutionary Metaheuristic (GEM) Algorithm for Engineering Optimization","translated_title":"","metadata":{"doi":"10.1080/23311916.2024.2364041","abstract":"Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":"Cogent Engineering"},"translated_abstract":"Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.","internal_url":"https://www.academia.edu/121710677/A_Generalized_Evolutionary_Metaheuristic_GEM_Algorithm_for_Engineering_Optimization","translated_internal_url":"","created_at":"2024-07-02T03:11:07.239-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":344652,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116526703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116526703/thumbnails/1.jpg","file_name":"Cogent_Eng_paper_preprint.pdf","download_url":"https://www.academia.edu/attachments/116526703/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Generalized_Evolutionary_Metaheuristic.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116526703/Cogent_Eng_paper_preprint-libre.pdf?1719918885=\u0026response-content-disposition=attachment%3B+filename%3DA_Generalized_Evolutionary_Metaheuristic.pdf\u0026Expires=1731455202\u0026Signature=clZ5Qt~KykT4YgPES3rONpuEj4OlNIaI6ymctpxMV2kNNw8wo70OCB2kaOAQEyp-Y-u~ACUxVetqpxIlDdgxZ9GiHcg~BHngnEsy4x2qM8hdme8SMzP~voRwDn28x73hBPhv2TxnqWm0JDdQcvT2BTQPfe63Rc3nTXXDLWEenDjx2ob2kLg7ERcbtveiIP8vPIiiH3y8jHbcX0mwoUYf1HkZyEXl4CUB1fZkPYzjptjS1GABEMxM2bl-YYQ5JAf-Y1~mTF2HPRkqrXG1cNhBEoMoTQVLCMccPzXBgReoMiiYhpHikOtjZBwbepycLvlduU9anlruHNCjJS4O9hjNRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Generalized_Evolutionary_Metaheuristic_GEM_Algorithm_for_Engineering_Optimization","translated_slug":"","page_count":17,"language":"en","content_type":"Work","owner":{"id":344652,"first_name":"Xin-She","middle_initials":null,"last_name":"Yang","page_name":"XinSheYang","domain_name":"cambridge","created_at":"2011-02-27T01:09:32.854-08:00","display_name":"Xin-She Yang","url":"https://cambridge.academia.edu/XinSheYang"},"attachments":[{"id":116526703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116526703/thumbnails/1.jpg","file_name":"Cogent_Eng_paper_preprint.pdf","download_url":"https://www.academia.edu/attachments/116526703/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Generalized_Evolutionary_Metaheuristic.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116526703/Cogent_Eng_paper_preprint-libre.pdf?1719918885=\u0026response-content-disposition=attachment%3B+filename%3DA_Generalized_Evolutionary_Metaheuristic.pdf\u0026Expires=1731455202\u0026Signature=clZ5Qt~KykT4YgPES3rONpuEj4OlNIaI6ymctpxMV2kNNw8wo70OCB2kaOAQEyp-Y-u~ACUxVetqpxIlDdgxZ9GiHcg~BHngnEsy4x2qM8hdme8SMzP~voRwDn28x73hBPhv2TxnqWm0JDdQcvT2BTQPfe63Rc3nTXXDLWEenDjx2ob2kLg7ERcbtveiIP8vPIiiH3y8jHbcX0mwoUYf1HkZyEXl4CUB1fZkPYzjptjS1GABEMxM2bl-YYQ5JAf-Y1~mTF2HPRkqrXG1cNhBEoMoTQVLCMccPzXBgReoMiiYhpHikOtjZBwbepycLvlduU9anlruHNCjJS4O9hjNRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation"},{"id":5395,"name":"Swarm Intelligence","url":"https://www.academia.edu/Documents/in/Swarm_Intelligence"},{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_"},{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques"},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing"},{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms"},{"id":810821,"name":"Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Nature_Inspired_Algorithms"},{"id":2628827,"name":"Derivative-free optimization","url":"https://www.academia.edu/Documents/in/Derivative-free_optimization"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="121710578"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/121710578/Parameter_Tuning_of_the_Firefly_Algorithm_by_Standard_Monte_Carlo_and_Quasi_Monte_Carlo_Methods"><img alt="Research paper thumbnail of Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods" class="work-thumbnail" src="https://attachments.academia-assets.com/116526656/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121710578/Parameter_Tuning_of_the_Firefly_Algorithm_by_Standard_Monte_Carlo_and_Quasi_Monte_Carlo_Methods">Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods</a></div><div class="wp-workCard_item"><span>ICCS 2024 paper</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such p...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure that the algorithm used for optimization performs well and is sufficiently robust for solving different types of optimization problems. In this study, the Firefly Algorithm (FA) is used to evaluate the influence of its parameter values on its efficiency. Parameter values are randomly initialized using both the standard Monte Carlo method and the Quasi Monte-Carlo method. The values are then used for tuning the FA. Two benchmark functions and a spring design problem are used to test the robustness of the tuned FA. From the preliminary findings, it can be deduced that both the Monte Carlo method and Quasi-Monte Carlo method produce similar results in terms of optimal fitness values. Numerical experiments using the two different methods on both benchmark functions and the spring design problem showed no major variations in the final fitness values, irrespective of the different sample values selected during the simulations. This insensitivity indicates the robustness of the FA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="404e8c91495cf6fae1d9da1795521993" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116526656,"asset_id":121710578,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116526656/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="121710578"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="121710578"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121710578; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121710578]").text(description); $(".js-view-count[data-work-id=121710578]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121710578; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121710578']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 121710578, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "404e8c91495cf6fae1d9da1795521993" } } $('.js-work-strip[data-work-id=121710578]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121710578,"title":"Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods","translated_title":"","metadata":{"doi":"10.1007/978-3-031-63775-9_17","abstract":"Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure that the algorithm used for optimization performs well and is sufficiently robust for solving different types of optimization problems. In this study, the Firefly Algorithm (FA) is used to evaluate the influence of its parameter values on its efficiency. Parameter values are randomly initialized using both the standard Monte Carlo method and the Quasi Monte-Carlo method. The values are then used for tuning the FA. Two benchmark functions and a spring design problem are used to test the robustness of the tuned FA. From the preliminary findings, it can be deduced that both the Monte Carlo method and Quasi-Monte Carlo method produce similar results in terms of optimal fitness values. Numerical experiments using the two different methods on both benchmark functions and the spring design problem showed no major variations in the final fitness values, irrespective of the different sample values selected during the simulations. This insensitivity indicates the robustness of the FA.","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":"ICCS 2024 paper"},"translated_abstract":"Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure that the algorithm used for optimization performs well and is sufficiently robust for solving different types of optimization problems. In this study, the Firefly Algorithm (FA) is used to evaluate the influence of its parameter values on its efficiency. Parameter values are randomly initialized using both the standard Monte Carlo method and the Quasi Monte-Carlo method. The values are then used for tuning the FA. Two benchmark functions and a spring design problem are used to test the robustness of the tuned FA. From the preliminary findings, it can be deduced that both the Monte Carlo method and Quasi-Monte Carlo method produce similar results in terms of optimal fitness values. Numerical experiments using the two different methods on both benchmark functions and the spring design problem showed no major variations in the final fitness values, irrespective of the different sample values selected during the simulations. This insensitivity indicates the robustness of the FA.","internal_url":"https://www.academia.edu/121710578/Parameter_Tuning_of_the_Firefly_Algorithm_by_Standard_Monte_Carlo_and_Quasi_Monte_Carlo_Methods","translated_internal_url":"","created_at":"2024-07-02T03:06:14.808-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":344652,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":41989989,"work_id":121710578,"tagging_user_id":344652,"tagged_user_id":176481693,"co_author_invite_id":null,"email":"g***d@gmail.com","affiliation":"Middlesex University","display_order":-1,"name":"geethu joy","title":"Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods"}],"downloadable_attachments":[{"id":116526656,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116526656/thumbnails/1.jpg","file_name":"Joy_Preprint_382.pdf","download_url":"https://www.academia.edu/attachments/116526656/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Parameter_Tuning_of_the_Firefly_Algorith.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116526656/Joy_Preprint_382-libre.pdf?1719918889=\u0026response-content-disposition=attachment%3B+filename%3DParameter_Tuning_of_the_Firefly_Algorith.pdf\u0026Expires=1732354819\u0026Signature=FGSiPgOmnOy3qXU~KwSoJi5lgBLjpBGd8hoNmqd6wUyYmi8kbGbqB9fiL2pZ03u35tjcKUjDsMvMpGehZIQdgj19TBcCQyud-N1fr6vtz4XoMCS0tp4CJVw1z4EKq9Wb6kyej6r1qu0QyvxxLHS4ftG1Bm1C7XT9ZpefESE2zvfXc-3Gc-oPOOf270d8l5pqrSh2XjjEdohpeXd~~yHMXXx9F2dIbTRvcgTm-4iZJSEr4lZmJ-P7Qn2pf5PXdvxCXCaOInPgBmR~oxhQw64ZVvIjUrc27MhP3unTrRVXAZFzIPBkwL6TUZcn1yFtIrUw5MQjfoLj8bAZ6uALPnucKA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Parameter_Tuning_of_the_Firefly_Algorithm_by_Standard_Monte_Carlo_and_Quasi_Monte_Carlo_Methods","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":344652,"first_name":"Xin-She","middle_initials":null,"last_name":"Yang","page_name":"XinSheYang","domain_name":"cambridge","created_at":"2011-02-27T01:09:32.854-08:00","display_name":"Xin-She 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Mathematical Modelling and Parameter Optimization of Pulsating Heat Pipes" class="work-thumbnail" src="https://attachments.academia-assets.com/116526627/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121710525/Mathematical_Modelling_and_Parameter_Optimization_of_Pulsating_Heat_Pipes">Mathematical Modelling and Parameter Optimization of Pulsating Heat Pipes</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Mar 30, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="511e9ea1c2c7ecb5da805f3d7ce626ee" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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href="https://www.academia.edu/121710519/Optimum_design_of_frame_structures_using_the_Eagle_Strategy_with_Differential_Evolution"><img alt="Research paper thumbnail of Optimum design of frame structures using the Eagle Strategy with Differential Evolution" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121710519/Optimum_design_of_frame_structures_using_the_Eagle_Strategy_with_Differential_Evolution">Optimum design of frame structures using the Eagle Strategy with Differential Evolution</a></div><div class="wp-workCard_item"><span>Engineering Structures</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Modern metaheuristic algorithms are in general suited for global optimization. This pape...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Modern metaheuristic algorithms are in general suited for global optimization. This paper combines the recently developed eagle strategy algorithm with differential evolution. The new algorithm, denoted as the ES–DE, is implemented by interfacing SAP2000 structural analysis code and MATLAB mathematical software. The performance of the ES–DE is evaluated by solving four benchmark problems where the objective is to minimize the weight of steel frames. The optimized designs obtained by the proposed algorithm are better than those found by the standard differential evolution algorithm and also very competitive with literature. 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/></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121710468/LibOPT_An_Open_Source_Platform_for_Fast_Prototyping_Soft_Optimization_Techniques">LibOPT: An Open-Source Platform for Fast Prototyping Soft Optimization Techniques</a></div><div class="wp-workCard_item"><span>ArXiv</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Optimization techniques play an important role in several scientific and real-world applications,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which ends up fostering the research and development of new techniques and applications. In this work, we present a new library for the implementation and fast prototyping of nature-inspired techniques called LibOPT. Currently, the library implements 15 techniques and 112 benchmarking functions, as well as it also supports 11 hypercomplex-based optimization approaches, which makes it one of the first of its kind. We showed how one can easily use and also implement new techniques in LibOPT under the C paradigm. Examples are provided with samples of source-code using benchmarking functions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="972faf25156c86d8111c8bd6a083c95b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116526586,"asset_id":121710468,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116526586/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="121710468"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="121710468"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121710468; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121710468]").text(description); $(".js-view-count[data-work-id=121710468]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121710468; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121710468']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 121710468, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "972faf25156c86d8111c8bd6a083c95b" } } $('.js-work-strip[data-work-id=121710468]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121710468,"title":"LibOPT: An Open-Source Platform for Fast Prototyping Soft Optimization Techniques","translated_title":"","metadata":{"abstract":"Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which ends up fostering the research and development of new techniques and applications. In this work, we present a new library for the implementation and fast prototyping of nature-inspired techniques called LibOPT. Currently, the library implements 15 techniques and 112 benchmarking functions, as well as it also supports 11 hypercomplex-based optimization approaches, which makes it one of the first of its kind. We showed how one can easily use and also implement new techniques in LibOPT under the C paradigm. Examples are provided with samples of source-code using benchmarking functions.","publisher":"ArXiv","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"ArXiv"},"translated_abstract":"Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which ends up fostering the research and development of new techniques and applications. In this work, we present a new library for the implementation and fast prototyping of nature-inspired techniques called LibOPT. Currently, the library implements 15 techniques and 112 benchmarking functions, as well as it also supports 11 hypercomplex-based optimization approaches, which makes it one of the first of its kind. We showed how one can easily use and also implement new techniques in LibOPT under the C paradigm. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="239635" id="teachingdocuments"><div class="js-work-strip profile--work_container" data-work-id="42392504"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/42392504/Multi_objective_Flower_Pollination_Algorithm_MOFPA_"><img alt="Research paper thumbnail of Multi-objective Flower Pollination Algorithm (MOFPA)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/42392504/Multi_objective_Flower_Pollination_Algorithm_MOFPA_">Multi-objective Flower Pollination Algorithm (MOFPA)</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">MOFPA--Multi-objective flower pollination algorithm. This demo solves a bi-objective ZDT function...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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.]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="50affa799b5e1debb1f75ebf07117b7a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62555717,"asset_id":42392504,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62555717/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="42392504"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="42392504"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42392504; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=42392504]").text(description); $(".js-view-count[data-work-id=42392504]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42392504; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='42392504']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 42392504, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "50affa799b5e1debb1f75ebf07117b7a" } } $('.js-work-strip[data-work-id=42392504]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":42392504,"title":"Multi-objective Flower Pollination Algorithm (MOFPA)","translated_title":"","metadata":{"doi":"10.1080/0305215x.2013.832237","abstract":"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.]","publication_date":{"day":null,"month":null,"year":2013,"errors":{}}},"translated_abstract":"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.]","internal_url":"https://www.academia.edu/42392504/Multi_objective_Flower_Pollination_Algorithm_MOFPA_","translated_internal_url":"","created_at":"2020-03-30T07:37:35.468-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":344652,"coauthors_can_edit":true,"document_type":"teaching_document","co_author_tags":[],"downloadable_attachments":[{"id":62555717,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"mofpa.txt","download_url":"https://www.academia.edu/attachments/62555717/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_objective_Flower_Pollination_Algor.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62555717/mofpa.txt?1585579575=\u0026response-content-disposition=attachment%3B+filename%3DMulti_objective_Flower_Pollination_Algor.txt\u0026Expires=1732354819\u0026Signature=I~K9DTXrKmY~1uKdnfxxhYePjK3W4BF4hR0Yu5KPsexucoN5lIwYEMacgKCuFEXXcxFapiATt9Hg6uj7Fk-sJi-wT574xtHTGWkWOZCAgBuPdBkhLTxATFZkKQJ1TYGwp~rf1eyOGCO9g~pHf4TSiJOvdZ6rh-jgeS-9-LZpPmQ6NEg8y9tHQ4eqVQj5fQgrlWXByN~m29NQQ4cFGc~yTe2WVNojqX7Ed8szJmv3IVcKqI~44a8bqXEFD-OmK6GD30~Qp4kG~ZisVcskr8dyffpH8TJzhyaXQbcnPlhQ9zzJ2Ocf2kVZMIEu~DKRNkOkjgc2x69IbmPX1vh8iJGN7w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Multi_objective_Flower_Pollination_Algorithm_MOFPA_","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":344652,"first_name":"Xin-She","middle_initials":null,"last_name":"Yang","page_name":"XinSheYang","domain_name":"cambridge","created_at":"2011-02-27T01:09:32.854-08:00","display_name":"Xin-She Yang","url":"https://cambridge.academia.edu/XinSheYang"},"attachments":[{"id":62555717,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"mofpa.txt","download_url":"https://www.academia.edu/attachments/62555717/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_objective_Flower_Pollination_Algor.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62555717/mofpa.txt?1585579575=\u0026response-content-disposition=attachment%3B+filename%3DMulti_objective_Flower_Pollination_Algor.txt\u0026Expires=1732354819\u0026Signature=I~K9DTXrKmY~1uKdnfxxhYePjK3W4BF4hR0Yu5KPsexucoN5lIwYEMacgKCuFEXXcxFapiATt9Hg6uj7Fk-sJi-wT574xtHTGWkWOZCAgBuPdBkhLTxATFZkKQJ1TYGwp~rf1eyOGCO9g~pHf4TSiJOvdZ6rh-jgeS-9-LZpPmQ6NEg8y9tHQ4eqVQj5fQgrlWXByN~m29NQQ4cFGc~yTe2WVNojqX7Ed8szJmv3IVcKqI~44a8bqXEFD-OmK6GD30~Qp4kG~ZisVcskr8dyffpH8TJzhyaXQbcnPlhQ9zzJ2Ocf2kVZMIEu~DKRNkOkjgc2x69IbmPX1vh8iJGN7w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_"},{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing"},{"id":86588,"name":"Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/Metaheuristic_Algorithms"},{"id":89916,"name":"Multi-Objective Optimization","url":"https://www.academia.edu/Documents/in/Multi-Objective_Optimization"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm"}],"urls":[{"id":8981694,"url":"https://uk.mathworks.com/matlabcentral/fileexchange/74750-multi-objective-flower-pollination-algorithm-mofpa"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="42392351"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/42392351/Multiobjective_Firefly_Algorithm_MOFA_"><img alt="Research paper thumbnail of Multiobjective Firefly Algorithm (MOFA)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/42392351/Multiobjective_Firefly_Algorithm_MOFA_">Multiobjective Firefly Algorithm (MOFA)</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The multiobjective firefly algorithm (MOFA) is a nature-inspired optimization algorithm. This dem...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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.]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ecb7b63a121ed868c2eae56f6f9ee0e7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62555293,"asset_id":42392351,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62555293/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="42392351"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="42392351"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42392351; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=42392351]").text(description); $(".js-view-count[data-work-id=42392351]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42392351; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='42392351']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 42392351, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ecb7b63a121ed868c2eae56f6f9ee0e7" } } $('.js-work-strip[data-work-id=42392351]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":42392351,"title":"Multiobjective Firefly Algorithm (MOFA)","translated_title":"","metadata":{"doi":"10.1007/s00366-012-0254-1","abstract":"The multiobjective firefly algorithm (MOFA) is a nature-inspired optimization algorithm. 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This demo so...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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.]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4bca294195e714eff80398bb95abf000" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62554269,"asset_id":42391482,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62554269/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="42391482"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><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); $(".js-view-count[data-work-id=42391482]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42391482; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='42391482']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 42391482, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4bca294195e714eff80398bb95abf000" } } $('.js-work-strip[data-work-id=42391482]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":42391482,"title":"Multiobjective Cuckoo Search (MOCS)","translated_title":"","metadata":{"doi":"10.1016/j.cor.2011.09.026","abstract":"The multiobjective cuckoo search (MOCS) is a nature-inspired optimization algorithm. 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S. Yang, Nature-Inspired Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/X._S._Yang_Nature-Inspired_Metaheuristic_Algorithms"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms"},{"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"},{"id":3603484,"name":"multiobjective cuckoo search","url":"https://www.academia.edu/Documents/in/multiobjective_cuckoo_search"}],"urls":[{"id":8981679,"url":"https://uk.mathworks.com/matlabcentral/fileexchange/74752-multiobjective-cuckoo-search-mocs"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7395156"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7395156/flower_pollination_algorithm_or_flower_algorithm_matlab_code"><img alt="Research paper thumbnail of flower pollination algorithm (or flower algorithm), matlab code" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" 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 class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The standard flower pollination algorithm (FPA) is inspired by the pollination characteristics of...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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. ]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d52dbbb8fa496d135cf3c873433b77ca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62552245,"asset_id":7395156,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62552245/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7395156"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7395156"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7395156; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7395156]").text(description); $(".js-view-count[data-work-id=7395156]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7395156; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7395156']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 7395156, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d52dbbb8fa496d135cf3c873433b77ca" } } $('.js-work-strip[data-work-id=7395156]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7395156,"title":"flower pollination algorithm (or flower algorithm), matlab code","translated_title":"","metadata":{"abstract":"The standard flower pollination algorithm (FPA) is inspired by the pollination characteristics of flowering plants. 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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. ]","internal_url":"https://www.academia.edu/7395156/flower_pollination_algorithm_or_flower_algorithm_matlab_code","translated_internal_url":"","created_at":"2014-06-18T22:43:30.679-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":344652,"coauthors_can_edit":true,"document_type":"teaching_document","co_author_tags":[],"downloadable_attachments":[{"id":62552245,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"fpa_demo_new.txt","download_url":"https://www.academia.edu/attachments/62552245/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"flower_pollination_algorithm_or_flower_a.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62552245/fpa_demo_new.txt?1585575245=\u0026response-content-disposition=attachment%3B+filename%3Dflower_pollination_algorithm_or_flower_a.txt\u0026Expires=1732354819\u0026Signature=XTh8JDjCZvDD-OLBgOM5982RRpHirsCULgLcuYfaHGulZk9h4z2of2rmJC1p3ccSAHfOgEai6QUk3mm5TnEmXf-ccDhVYyNq1B3X9oW80hcATnhdcltlyJuySQsTDYdUeJ9ugzOFs8gB2thtxfcLkZk-dlfGpF0-I8Jpw-Akhpqw8MNHnjYyy9ecAisJqyEKmxktbpyeCexNkFHKNtFKrVRgHzK6HE7YmOMatmTKpHpxQHvv6ISrZPR4q1g1vVyjb2xj34oryGgh2N7VU7B5Ku~x09x8ZaQa~rIAU1~mvJnvDBd7975a5Lo4B84d4VAuFB-7EJqb4d7tzO8J9iuaMw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"flower_pollination_algorithm_or_flower_algorithm_matlab_code","translated_slug":"","page_count":3,"language":"en","content_type":"Work","owner":{"id":344652,"first_name":"Xin-She","middle_initials":null,"last_name":"Yang","page_name":"XinSheYang","domain_name":"cambridge","created_at":"2011-02-27T01:09:32.854-08:00","display_name":"Xin-She Yang","url":"https://cambridge.academia.edu/XinSheYang"},"attachments":[{"id":62552245,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"fpa_demo_new.txt","download_url":"https://www.academia.edu/attachments/62552245/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"flower_pollination_algorithm_or_flower_a.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62552245/fpa_demo_new.txt?1585575245=\u0026response-content-disposition=attachment%3B+filename%3Dflower_pollination_algorithm_or_flower_a.txt\u0026Expires=1732354819\u0026Signature=XTh8JDjCZvDD-OLBgOM5982RRpHirsCULgLcuYfaHGulZk9h4z2of2rmJC1p3ccSAHfOgEai6QUk3mm5TnEmXf-ccDhVYyNq1B3X9oW80hcATnhdcltlyJuySQsTDYdUeJ9ugzOFs8gB2thtxfcLkZk-dlfGpF0-I8Jpw-Akhpqw8MNHnjYyy9ecAisJqyEKmxktbpyeCexNkFHKNtFKrVRgHzK6HE7YmOMatmTKpHpxQHvv6ISrZPR4q1g1vVyjb2xj34oryGgh2N7VU7B5Ku~x09x8ZaQa~rIAU1~mvJnvDBd7975a5Lo4B84d4VAuFB-7EJqb4d7tzO8J9iuaMw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms"},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm"}],"urls":[{"id":8981627,"url":"https://uk.mathworks.com/matlabcentral/fileexchange/74765-the-standard-flower-pollination-algorithm-fpa"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7395149"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7395149/Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_"><img alt="Research paper thumbnail of Multi-objective Bat Algorithm (MOBA) demo (Matlab code)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" 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 class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo so...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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.]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f3f40f5d1a076ea324d9f901dce0e704" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62552813,"asset_id":7395149,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62552813/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7395149"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7395149"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7395149; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7395149]").text(description); $(".js-view-count[data-work-id=7395149]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7395149; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7395149']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 7395149, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f3f40f5d1a076ea324d9f901dce0e704" } } $('.js-work-strip[data-work-id=7395149]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7395149,"title":"Multi-objective Bat Algorithm (MOBA) demo (Matlab code)","translated_title":"","metadata":{"abstract":"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.\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.]","more_info":"Xin-She Yang","event_date":{"day":null,"month":null,"year":2011,"errors":{}}},"translated_abstract":"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.\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.]","internal_url":"https://www.academia.edu/7395149/Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_","translated_internal_url":"","created_at":"2014-06-18T22:42:42.050-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":344652,"coauthors_can_edit":true,"document_type":"teaching_document","co_author_tags":[],"downloadable_attachments":[{"id":62552813,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"moba.txt","download_url":"https://www.academia.edu/attachments/62552813/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_objective_Bat_Algorithm_MOBA_demo.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62552813/moba.txt?1585576126=\u0026response-content-disposition=attachment%3B+filename%3DMulti_objective_Bat_Algorithm_MOBA_demo.txt\u0026Expires=1732354819\u0026Signature=FgcLvc3EMe~wv6RXbElwJnWPssrs1~WHKiIochsR0sA57QZFxdZMxRH2BxTyvQLfuhK0Fa73wA4bnZmLua5ThSOeFqSJAKyINJsfroCKvUjwdz9AqvOsEMHao8BpThqi7AmAvZY3FRCOOPGLTp6UgHcGaL~GHFLpKwXV36aUxAcYkEELr6B9WXxiMvlui8jCc6m4ag6TopVdTUt3Q5NknWBIhWv5Sqy~8~19s0TJHcLAofL1PEV4FD8fDj7cI~yViDm5FFmwMAX6U~BiYR8UGB8pGrHwKL2zbCgGt3ASdqVxlxXpha9bdKaRZuGT1akgB1dupwjW0FEq2C~5645OMQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":344652,"first_name":"Xin-She","middle_initials":null,"last_name":"Yang","page_name":"XinSheYang","domain_name":"cambridge","created_at":"2011-02-27T01:09:32.854-08:00","display_name":"Xin-She Yang","url":"https://cambridge.academia.edu/XinSheYang"},"attachments":[{"id":62552813,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"moba.txt","download_url":"https://www.academia.edu/attachments/62552813/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_objective_Bat_Algorithm_MOBA_demo.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62552813/moba.txt?1585576126=\u0026response-content-disposition=attachment%3B+filename%3DMulti_objective_Bat_Algorithm_MOBA_demo.txt\u0026Expires=1732354819\u0026Signature=FgcLvc3EMe~wv6RXbElwJnWPssrs1~WHKiIochsR0sA57QZFxdZMxRH2BxTyvQLfuhK0Fa73wA4bnZmLua5ThSOeFqSJAKyINJsfroCKvUjwdz9AqvOsEMHao8BpThqi7AmAvZY3FRCOOPGLTp6UgHcGaL~GHFLpKwXV36aUxAcYkEELr6B9WXxiMvlui8jCc6m4ag6TopVdTUt3Q5NknWBIhWv5Sqy~8~19s0TJHcLAofL1PEV4FD8fDj7cI~yViDm5FFmwMAX6U~BiYR8UGB8pGrHwKL2zbCgGt3ASdqVxlxXpha9bdKaRZuGT1akgB1dupwjW0FEq2C~5645OMQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":62552828,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"moba.txt","download_url":"https://www.academia.edu/attachments/62552828/download_file","bulk_download_file_name":"Multi_objective_Bat_Algorithm_MOBA_demo.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62552828/moba.txt?1585576138=\u0026response-content-disposition=attachment%3B+filename%3DMulti_objective_Bat_Algorithm_MOBA_demo.txt\u0026Expires=1732354819\u0026Signature=MT5wk6OKjI~FLV6GB4oU07SLJdi5i~0B3FBo7yHhztXkRB-IcOMT2k9XmZAV2dSErAmJCZSLjzsUAfIPz9wythcZpjdveLG~RqiCY5rPklkzNW~j8gvx65GAExeJGaq2S5iO54-EYnMLHsUvqabbD6byPSMIBd7jDo31Wwl0AC0S~1e6kSkhkE2pKLtFMydtJEZ0~TpRHgU7j6Jy0oR6sLSXN0Jm8fRlPYndbwBjZo4v95MCyxthMh54HWO3DzQOoz1Nh2IpZHt34ObbObBQvPdxf89oV3VCrx-1M7PMkMSlvj-YFIjUx4SIsnAv80iAnZaDD6pYMjx8X9rwiB-1cg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":898,"name":"Multiculturalism","url":"https://www.academia.edu/Documents/in/Multiculturalism"},{"id":10924,"name":"Optimization techniques","url":"https://www.academia.edu/Documents/in/Optimization_techniques"},{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization"},{"id":56605,"name":"Multiobjective Evolutionary Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Evolutionary_Optimization"},{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing"},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms"},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm"}],"urls":[{"id":8981652,"url":"https://uk.mathworks.com/matlabcentral/fileexchange/74753-multiobjective-bat-algorithm-moba"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2209187"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2209187/Bat_algorithm"><img alt="Research paper thumbnail of Bat algorithm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2209187/Bat_algorithm">Bat algorithm</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The standard bat algorithm (BA) is inspired by the echolocation characteristics of microbats. Thi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The standard bat algorithm (BA) is inspired by the echolocation characteristics of microbats. 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. ]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="dd8df6a58c84135c7ca0240e19a14867" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62553389,"asset_id":2209187,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62553389/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2209187"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2209187"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2209187; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2209187]").text(description); $(".js-view-count[data-work-id=2209187]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2209187; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2209187']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 2209187, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "dd8df6a58c84135c7ca0240e19a14867" } } $('.js-work-strip[data-work-id=2209187]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2209187,"title":"Bat algorithm","translated_title":"","metadata":{"abstract":"The standard bat algorithm (BA) is inspired by the echolocation characteristics of microbats. 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. ]"},"translated_abstract":"The standard bat algorithm (BA) is inspired by the echolocation characteristics of microbats. 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. ]","internal_url":"https://www.academia.edu/2209187/Bat_algorithm","translated_internal_url":"","created_at":"2012-11-27T22:39:20.063-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":344652,"coauthors_can_edit":true,"document_type":"teaching_document","co_author_tags":[],"downloadable_attachments":[{"id":62553389,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"bat_algorithm_new.txt","download_url":"https://www.academia.edu/attachments/62553389/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Bat_algorithm.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62553389/bat_algorithm_new.txt?1585576851=\u0026response-content-disposition=attachment%3B+filename%3DBat_algorithm.txt\u0026Expires=1732354819\u0026Signature=fvNMm5qPJXlLVw-e77hniFeInbPPwePPrTFZ2br64Zv0RqtmaHoFfDintluz0cT5d-s62cFMkiXb8HmUN6gaW0V5XyjazitTajcR13lWoirmpLZ97gveLZxiia12~iYvcP3DvAkEraNw1gs7Gtwym9JE4lpWfZZ-430hXuerrXscNohIOfGOh4IwevzqalQoY9b21MUQLmwRGlnbYAmXM99H3zhHwMDnoAMvUgNnuhbwCdt--J9NZwMJn5v4kRT6ttHGxCHmnfS2Q0W0mYNpxHL92cokza7v9ILulcllgE85FvPsSQmi5gnGaQv5HlRhU95xxJYduOMmTk6kM7SncQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Bat_algorithm","translated_slug":"","page_count":2,"language":"en","content_type":"Work","owner":{"id":344652,"first_name":"Xin-She","middle_initials":null,"last_name":"Yang","page_name":"XinSheYang","domain_name":"cambridge","created_at":"2011-02-27T01:09:32.854-08:00","display_name":"Xin-She Yang","url":"https://cambridge.academia.edu/XinSheYang"},"attachments":[{"id":62553389,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"bat_algorithm_new.txt","download_url":"https://www.academia.edu/attachments/62553389/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Bat_algorithm.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62553389/bat_algorithm_new.txt?1585576851=\u0026response-content-disposition=attachment%3B+filename%3DBat_algorithm.txt\u0026Expires=1732354819\u0026Signature=fvNMm5qPJXlLVw-e77hniFeInbPPwePPrTFZ2br64Zv0RqtmaHoFfDintluz0cT5d-s62cFMkiXb8HmUN6gaW0V5XyjazitTajcR13lWoirmpLZ97gveLZxiia12~iYvcP3DvAkEraNw1gs7Gtwym9JE4lpWfZZ-430hXuerrXscNohIOfGOh4IwevzqalQoY9b21MUQLmwRGlnbYAmXM99H3zhHwMDnoAMvUgNnuhbwCdt--J9NZwMJn5v4kRT6ttHGxCHmnfS2Q0W0mYNpxHL92cokza7v9ILulcllgE85FvPsSQmi5gnGaQv5HlRhU95xxJYduOMmTk6kM7SncQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":6413,"name":"Metaheuristics (Operations Research)","url":"https://www.academia.edu/Documents/in/Metaheuristics_Operations_Research_"},{"id":131957,"name":"Bat Algorithm","url":"https://www.academia.edu/Documents/in/Bat_Algorithm"},{"id":649904,"name":"Nature-inpsired Computing","url":"https://www.academia.edu/Documents/in/Nature-inpsired_Computing"}],"urls":[{"id":8981664,"url":"https://uk.mathworks.com/matlabcentral/fileexchange/74768-the-standard-bat-algorithm-ba"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1739427"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1739427/Accelerated_Particle_Swarm_Optimization_APSO_"><img alt="Research paper thumbnail of Accelerated Particle Swarm Optimization (APSO)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1739427/Accelerated_Particle_Swarm_Optimization_APSO_">Accelerated Particle Swarm Optimization (APSO)</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The accelerated particle swarm optimization (APSO) uses only the global best without individual b...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The accelerated particle swarm optimization (APSO) uses only the global best without individual best solutions and reduced randomness. This demo solves a function of D=30 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. ]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f70bb131b6d2057bd2ab40693db347a9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62553541,"asset_id":1739427,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62553541/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1739427"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1739427"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1739427; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1739427]").text(description); $(".js-view-count[data-work-id=1739427]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1739427; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1739427']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 1739427, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f70bb131b6d2057bd2ab40693db347a9" } } $('.js-work-strip[data-work-id=1739427]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1739427,"title":"Accelerated Particle Swarm Optimization (APSO)","translated_title":"","metadata":{"abstract":"The accelerated particle swarm optimization (APSO) uses only the global best without individual best solutions and reduced randomness. This demo solves a function of D=30 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. ]\r\n","more_info":"This is an accelerated PSO (APSO), developed by Xin-She Yang in 2008. APSO does not use velocities or any inertia parameter.\nA full program for solving nonlinear constrained optimization problem (welded beam design as an example) is provided, which can be extended to solve other continuous optimization problems."},"translated_abstract":"The accelerated particle swarm optimization (APSO) uses only the global best without individual best solutions and reduced randomness. This demo solves a function of D=30 dimensions. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1739424"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1739424/Firefly_Algorithm"><img alt="Research paper thumbnail of Firefly Algorithm " class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1739424/Firefly_Algorithm">Firefly Algorithm </a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The standard firefly algorithm is inspired by the flashing patterns of tropical fireflies. This d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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. ]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="782c33b639148cce12fa7f61e710a75b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62553677,"asset_id":1739424,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62553677/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1739424"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><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); $(".js-view-count[data-work-id=1739424]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1739424; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1739424']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 1739424, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "782c33b639148cce12fa7f61e710a75b" } } $('.js-work-strip[data-work-id=1739424]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1739424,"title":"Firefly Algorithm ","translated_title":"","metadata":{"abstract":"The standard firefly algorithm is inspired by the flashing patterns of tropical fireflies. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1739423"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1739423/Cuckoo_search_algorithm"><img alt="Research paper thumbnail of Cuckoo search algorithm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1739423/Cuckoo_search_algorithm">Cuckoo search algorithm</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The standard cuckoo search algorithm is inspired by the evolutionary characteristics of cuckoo-ho...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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. ]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fb44327795d4dd8013c7e710fc8e73d0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62553751,"asset_id":1739423,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62553751/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1739423"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><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); $(".js-view-count[data-work-id=1739423]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1739423; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1739423']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 1739423, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fb44327795d4dd8013c7e710fc8e73d0" } } $('.js-work-strip[data-work-id=1739423]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1739423,"title":"Cuckoo search algorithm","translated_title":"","metadata":{"abstract":"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. ]","more_info":"A new metaheuristic optimization algorithm, called Cuckoo Search (CS), is fully implemented, and the vectorized version is given here. This code demonstrates how CS works for unconstrained optimization, which can easily be extended to solve various global optimization problems efficiently."},"translated_abstract":"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. ]","internal_url":"https://www.academia.edu/1739423/Cuckoo_search_algorithm","translated_internal_url":"","created_at":"2011-03-01T18:20:26.485-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":344652,"coauthors_can_edit":true,"document_type":"teaching_document","co_author_tags":[],"downloadable_attachments":[{"id":62553751,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"cuckoo_search_new.txt","download_url":"https://www.academia.edu/attachments/62553751/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Cuckoo_search_algorithm.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62553751/cuckoo_search_new.txt?1585577250=\u0026response-content-disposition=attachment%3B+filename%3DCuckoo_search_algorithm.txt\u0026Expires=1732354820\u0026Signature=CEbqovudwc5p5J~bt2s8lYiv~EIH00KAOT4ohaVLdi2z1~2cJt78F8MfzEh-UH8WrToJQpLl17z3SaEAOLFWNJEDsYvv6LqM6B~sku758W8Vk4IJq6wWspzNueNGry-zT7Fx9Mh7wW-TIB~Btam0kb9pG5OWr4CZMEZ3b~pWs12QWoRPObDPrhcNNX1A3zPQWRmzRkazOOu9P89qQcyeQAujdzthG8PBMskeWjoq4K-WB9LpFSHRrOybMVNJxBh6ID93rzM4mNU7WI0tzEA0pxWN2h3EghaBcaee47NcTgDgCfkbrUmdx4mRzcwjFpFCYg1B4i4Kc9KF5VsyM94lKg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Cuckoo_search_algorithm","translated_slug":"","page_count":3,"language":"en","content_type":"Work","owner":{"id":344652,"first_name":"Xin-She","middle_initials":null,"last_name":"Yang","page_name":"XinSheYang","domain_name":"cambridge","created_at":"2011-02-27T01:09:32.854-08:00","display_name":"Xin-She Yang","url":"https://cambridge.academia.edu/XinSheYang"},"attachments":[{"id":62553751,"title":"","file_type":"txt","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"cuckoo_search_new.txt","download_url":"https://www.academia.edu/attachments/62553751/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Cuckoo_search_algorithm.txt","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/62553751/cuckoo_search_new.txt?1585577250=\u0026response-content-disposition=attachment%3B+filename%3DCuckoo_search_algorithm.txt\u0026Expires=1732354820\u0026Signature=CEbqovudwc5p5J~bt2s8lYiv~EIH00KAOT4ohaVLdi2z1~2cJt78F8MfzEh-UH8WrToJQpLl17z3SaEAOLFWNJEDsYvv6LqM6B~sku758W8Vk4IJq6wWspzNueNGry-zT7Fx9Mh7wW-TIB~Btam0kb9pG5OWr4CZMEZ3b~pWs12QWoRPObDPrhcNNX1A3zPQWRmzRkazOOu9P89qQcyeQAujdzthG8PBMskeWjoq4K-WB9LpFSHRrOybMVNJxBh6ID93rzM4mNU7WI0tzEA0pxWN2h3EghaBcaee47NcTgDgCfkbrUmdx4mRzcwjFpFCYg1B4i4Kc9KF5VsyM94lKg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":84562,"name":"Nature-Inspired Computing","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Computing"},{"id":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search"},{"id":278933,"name":"X. S. Yang, Nature-Inspired Metaheuristic Algorithms","url":"https://www.academia.edu/Documents/in/X._S._Yang_Nature-Inspired_Metaheuristic_Algorithms"},{"id":423243,"name":"Bio and Nature Inspired Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms"},{"id":1330965,"name":"Cuckoo Search Algorithm","url":"https://www.academia.edu/Documents/in/Cuckoo_Search_Algorithm"}],"urls":[{"id":8981669,"url":"https://uk.mathworks.com/matlabcentral/fileexchange/74767-the-standard-cuckoo-search-cs"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="5847108" id="talks"><div class="js-work-strip profile--work_container" data-work-id="42688203"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/42688203/Nature_Inspired_Optimization_Algorithms_Introduction_and_Overview"><img alt="Research paper thumbnail of Nature-Inspired Optimization Algorithms: Introduction and Overview" class="work-thumbnail" src="https://attachments.academia-assets.com/62898672/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/42688203/Nature_Inspired_Optimization_Algorithms_Introduction_and_Overview">Nature-Inspired Optimization Algorithms: Introduction and Overview</a></div><div class="wp-workCard_item"><span>Nature-Inspired Optimization Algorithms</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This presentation introduces the fundamental ideas of nature-inspired optimization algorithms, ba...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="707d77f717451f16b75fb8db84784a29" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62898672,"asset_id":42688203,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62898672/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="42688203"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" 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Algorithms","url":"https://www.academia.edu/Documents/in/Bio_and_Nature_Inspired_Algorithms"},{"id":544056,"name":"Nature-Inspired Algorithm","url":"https://www.academia.edu/Documents/in/Nature-Inspired_Algorithm"},{"id":1421559,"name":"Flower Pollination Algorithm","url":"https://www.academia.edu/Documents/in/Flower_Pollination_Algorithm"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="42682955"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/42682955/Flower_Pollination_Algorithm_An_Introduction"><img alt="Research paper thumbnail of Flower Pollination Algorithm: An Introduction" class="work-thumbnail" src="https://attachments.academia-assets.com/62899444/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/42682955/Flower_Pollination_Algorithm_An_Introduction">Flower Pollination Algorithm: An Introduction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This presentation explains the fundamental ideas of the standard Flower Pollination Algorithm (FP...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated 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.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="53903f2c0fa6416428dab14ae7b6c2ea" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":62899444,"asset_id":42682955,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/62899444/download_file?st=MTczMjM3NjQzMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="42682955"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="42682955"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42682955; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=42682955]").text(description); $(".js-view-count[data-work-id=42682955]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42682955; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='42682955']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 42682955, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "53903f2c0fa6416428dab14ae7b6c2ea" } } $('.js-work-strip[data-work-id=42682955]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":42682955,"title":"Flower Pollination Algorithm: An Introduction","translated_title":"","metadata":{"abstract":"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). 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