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Khan Mashwani</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://kust.academia.edu/">Kohat University of science and technology</a>, <a class="u-tcGrayDarker" href="https://kust.academia.edu/Departments/Dept_of_mathematics/Documents">Dept. of mathematics</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Prof.Dr.Wali Khan" data-follow-user-id="35135010" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" 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Physical and Numerical Sciences,Director Institute of Numerical Sciences, Academic Block-III,Kohat University of Science<br /><span class="u-fw700">Supervisors: </span>Professor Qingfu Zhang, University of Essex, Clochester, UK<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="35135010" href="https://www.academia.edu/Documents/in/Moral_emotions"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://kust.academia.edu/WaliMashwani","location":"/WaliMashwani","scheme":"https","host":"kust.academia.edu","port":null,"pathname":"/WaliMashwani","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> 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profile--work_container" data-work-id="118935219"><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/118935219/The_State_of_AI_Empowered_Backscatter_Communications_A_Comprehensive_Survey"><img alt="Research paper thumbnail of The State of AI-Empowered Backscatter Communications: A Comprehensive Survey" class="work-thumbnail" src="https://attachments.academia-assets.com/114441307/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/118935219/The_State_of_AI_Empowered_Backscatter_Communications_A_Comprehensive_Survey">The State of AI-Empowered Backscatter Communications: A Comprehensive Survey</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper brings these two technologies together to investigate the current state of AI-powered ...</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 paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with RIS, UAV, and vehicular networks. Finally, we highlight the challenges and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fd00ac8022a24aabce7f18dc0fa56da5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":114441307,"asset_id":118935219,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/114441307/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="118935219"><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="118935219"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118935219; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118935219]").text(description); $(".js-view-count[data-work-id=118935219]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118935219; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118935219']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118935219, 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: "fd00ac8022a24aabce7f18dc0fa56da5" } } $('.js-work-strip[data-work-id=118935219]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118935219,"title":"The State of AI-Empowered Backscatter Communications: A Comprehensive Survey","translated_title":"","metadata":{"abstract":"This paper brings these two technologies together to investigate the current state of AI-powered BC. 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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="118935217"><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/118935217/Applications_of_q_Derivative_Operator_to_the_Subclass_of_Bi_Univalent_Functions_Involving_q_Chebyshev_Polynomials"><img alt="Research paper thumbnail of Applications of q -Derivative Operator to the Subclass of Bi-Univalent Functions Involving q -Chebyshev Polynomials" class="work-thumbnail" src="https://attachments.academia-assets.com/114441304/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/118935217/Applications_of_q_Derivative_Operator_to_the_Subclass_of_Bi_Univalent_Functions_Involving_q_Chebyshev_Polynomials">Applications of q -Derivative Operator to the Subclass of Bi-Univalent Functions Involving q -Chebyshev Polynomials</a></div><div class="wp-workCard_item"><span>Journal of Mathematics</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In recent years, the usage of the q -derivative and symmetric q -derivative operators is signific...</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">In recent years, the usage of the q -derivative and symmetric q -derivative operators is significant. In this study, firstly, many known concepts of the q -derivative operator are highlighted and given. We then use the symmetric q -derivative operator and certain q -Chebyshev polynomials to define a new subclass of analytic and bi-univalent functions. For this newly defined functions’ classes, a number of coefficient bounds, along with the Fekete–Szegö inequalities, are also given. To validate our results, we give some known consequences in form of remarks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="16c9b2d33eb917004d49523e96912462" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":114441304,"asset_id":118935217,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/114441304/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="118935217"><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="118935217"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118935217; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118935217]").text(description); $(".js-view-count[data-work-id=118935217]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118935217; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118935217']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118935217, 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: "16c9b2d33eb917004d49523e96912462" } } $('.js-work-strip[data-work-id=118935217]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118935217,"title":"Applications of q -Derivative Operator to the Subclass of Bi-Univalent Functions Involving q -Chebyshev Polynomials","translated_title":"","metadata":{"abstract":"In recent years, the usage of the q -derivative and symmetric q -derivative operators is significant. 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For this newly defined functions’ classes, a number of coefficient bounds, along with the Fekete–Szegö inequalities, are also given. To validate our results, we give some known consequences in form of remarks.","internal_url":"https://www.academia.edu/118935217/Applications_of_q_Derivative_Operator_to_the_Subclass_of_Bi_Univalent_Functions_Involving_q_Chebyshev_Polynomials","translated_internal_url":"","created_at":"2024-05-11T22:27:13.607-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":114441304,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441304/thumbnails/1.jpg","file_name":"8162182.pdf","download_url":"https://www.academia.edu/attachments/114441304/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Applications_of_q_Derivative_Operator_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441304/8162182-libre.pdf?1715492478=\u0026response-content-disposition=attachment%3B+filename%3DApplications_of_q_Derivative_Operator_to.pdf\u0026Expires=1732704025\u0026Signature=Y6FOHSpd2Y1H4l28eMdxdZznVtfYJf2dP8MKlPwRhAiO6aEpOJuGxm7FsNmWU9JcpOhknMdNL6UZvWcv-tmYvfZq4lNx45eXxuRzmSdWF2dCvh3zfF5yFKy5-1MxN85w7xxgna9M3cKYNbtlUw1hcrspXjZGW2TXpsiw3PK6oWBNqvCEMwE6tPz8ALVbD5aViMiGNPKb-H9x1idVJywzBAUX5iJm8wtjuO35mc9Co7cP5JV2oCCoDk1-Z3YypvpSsXRvPpYWPQ9B1h3Ip-NEKG8vgP6pq3EYrRY~psF1oxhEwMiNz0hauwz7qrkxtHjFsYeHeHVwZD~41Qr5hPhOOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Applications_of_q_Derivative_Operator_to_the_Subclass_of_Bi_Univalent_Functions_Involving_q_Chebyshev_Polynomials","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[{"id":114441304,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441304/thumbnails/1.jpg","file_name":"8162182.pdf","download_url":"https://www.academia.edu/attachments/114441304/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Applications_of_q_Derivative_Operator_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441304/8162182-libre.pdf?1715492478=\u0026response-content-disposition=attachment%3B+filename%3DApplications_of_q_Derivative_Operator_to.pdf\u0026Expires=1732704025\u0026Signature=Y6FOHSpd2Y1H4l28eMdxdZznVtfYJf2dP8MKlPwRhAiO6aEpOJuGxm7FsNmWU9JcpOhknMdNL6UZvWcv-tmYvfZq4lNx45eXxuRzmSdWF2dCvh3zfF5yFKy5-1MxN85w7xxgna9M3cKYNbtlUw1hcrspXjZGW2TXpsiw3PK6oWBNqvCEMwE6tPz8ALVbD5aViMiGNPKb-H9x1idVJywzBAUX5iJm8wtjuO35mc9Co7cP5JV2oCCoDk1-Z3YypvpSsXRvPpYWPQ9B1h3Ip-NEKG8vgP6pq3EYrRY~psF1oxhEwMiNz0hauwz7qrkxtHjFsYeHeHVwZD~41Qr5hPhOOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":114441303,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441303/thumbnails/1.jpg","file_name":"8162182.pdf","download_url":"https://www.academia.edu/attachments/114441303/download_file","bulk_download_file_name":"Applications_of_q_Derivative_Operator_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441303/8162182-libre.pdf?1715492478=\u0026response-content-disposition=attachment%3B+filename%3DApplications_of_q_Derivative_Operator_to.pdf\u0026Expires=1732704025\u0026Signature=cgXIjrmQJZNfwtKzSXIr0J8kkwf2ONplDQ6KqZxvzlX6pUGgNValThxJzJS3gCMAcBo5RD5yPe9EJGCBorCiMC7LMRFTGgJt~33HVaAr4tX0esGfUnPHjFTauSh0jAfPUkV5vtsOBEbA6qqfFK79~-aX7dUFKWLrAz59kgAl4QVzV~AG5ApmItLDAu5r8rKd1hf~MvVhEmDHB0~JStx~085ig-tToAiMNqTb2x28khD~XDmNmQCac7zRA5B~F0njyzTHR3O~xH~-IQFshrcRQsbqI0jC7U-rgFDfhygoQdq91RDVkalb2XK-56skX~PBbY6ULezTH1LrkXCpR7dB~Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"}],"urls":[{"id":41865770,"url":"http://downloads.hindawi.com/journals/jmath/2022/8162182.pdf"}]}, dispatcherData: dispatcherData }); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d707c7f78b5568b7ff9ad09dcb5448a9" } } $('.js-work-strip[data-work-id=118935216]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118935216,"title":"Reflected Adaptive Differential Evolution with Two External Archives for Large-Scale Global Optimization","translated_title":"","metadata":{"publisher":"The Science and Information Organization","grobid_abstract":"JADE is an adaptive scheme of nature inspired algorithm, Differential Evolution (DE). It performed considerably improved on a set of well-studied benchmark test problems. In this paper, we evaluate the performance of new JADE with two external archives to deal with unconstrained continuous large-scale global optimization problems labeled as Reflected Adaptive Differential Evolution with Two External Archives (RJADE/TA). The only archive of JADE stores failed solutions. In contrast, the proposed second archive stores superior solutions at regular intervals of the optimization process to avoid premature convergence towards local optima. The superior solutions which are sent to the archive are reflected by new potential solutions. At the end of the search process, the best solution is selected from the second archive and the current population. The performance of RJADE/TA algorithm is then extensively evaluated on two test beds. At first on 28 latest benchmark functions constructed for the 2013 Congress on Evolutionary Computation special session. Secondly on ten benchmark problems from CEC2010 Special Session and Competition on Large-Scale Global Optimization. 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MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different problems. The use of multiple operators with a selfadaptive capability can further improve the performance of existing MOEAs. This paper suggests an enhanced version of a genetically adaptive multi-algorithm for multi-objective (AMAL-GAM) optimisation which includes differential evolution (DE), particle swarm optimization (PSO), simulated binary crossover (SBX), Pareto archive evolution strategy (PAES) and simplex crossover (SPX) for population evolution during the course of optimization. We examine the performance of this enhanced version of AMALGAM experimentally over two different test suites, the ZDT test problems and the test instances designed recently for the special session on MOEA's competition at the Congress of Evolutionary Computing of 2009 (CEC'09). The suggested algorithm has found better approximate solutions on most test problems in terms of inverted generational distance (IGD) as the metric indicator.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"International Journal of Advanced Computer Science and Applications","grobid_abstract_attachment_id":114441322},"translated_abstract":null,"internal_url":"https://www.academia.edu/118935215/Enhanced_Version_of_Multi_algorithm_Genetically_Adaptive_for_Multiobjective_optimization","translated_internal_url":"","created_at":"2024-05-11T22:27:12.565-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":114441322,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441322/thumbnails/1.jpg","file_name":"084a466120acb26f1ce748f3ea27b931cd90.pdf","download_url":"https://www.academia.edu/attachments/114441322/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Enhanced_Version_of_Multi_algorithm_Gene.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441322/084a466120acb26f1ce748f3ea27b931cd90-libre.pdf?1715492866=\u0026response-content-disposition=attachment%3B+filename%3DEnhanced_Version_of_Multi_algorithm_Gene.pdf\u0026Expires=1732704026\u0026Signature=BYTkvlMadIdeGbkgAZYEaRL2rNAdZBXfiZJgiR6Z08UO-qGAcKqmjrdcGR4xzW6Pl6HraLMO7Bf4NuFy575DkQlzy1npRstZRlciAVzvCe~rQnqhhwYwLHJr8M6XGyYLkALUW3UCH2x-W-W5lU4hpFo8dwZwqTfpIsLfjVBwsXdHNl0Qh~89e3n2p0GDcEbvjMiHQ4kBrRJzjHPCbA0fL5sVRi68xdRpfPfqySp17FGvmWd2escTwUrOS2CfBpFdhOLM9ZOcQfaUvMUPoYox6c9hTMVct0OuN89mG-xE134m4ypAkcc-EZaS9ET-3wttjCCy0pPt13jOVl3N57eJHA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Enhanced_Version_of_Multi_algorithm_Genetically_Adaptive_for_Multiobjective_optimization","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[{"id":114441322,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441322/thumbnails/1.jpg","file_name":"084a466120acb26f1ce748f3ea27b931cd90.pdf","download_url":"https://www.academia.edu/attachments/114441322/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Enhanced_Version_of_Multi_algorithm_Gene.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441322/084a466120acb26f1ce748f3ea27b931cd90-libre.pdf?1715492866=\u0026response-content-disposition=attachment%3B+filename%3DEnhanced_Version_of_Multi_algorithm_Gene.pdf\u0026Expires=1732704026\u0026Signature=BYTkvlMadIdeGbkgAZYEaRL2rNAdZBXfiZJgiR6Z08UO-qGAcKqmjrdcGR4xzW6Pl6HraLMO7Bf4NuFy575DkQlzy1npRstZRlciAVzvCe~rQnqhhwYwLHJr8M6XGyYLkALUW3UCH2x-W-W5lU4hpFo8dwZwqTfpIsLfjVBwsXdHNl0Qh~89e3n2p0GDcEbvjMiHQ4kBrRJzjHPCbA0fL5sVRi68xdRpfPfqySp17FGvmWd2escTwUrOS2CfBpFdhOLM9ZOcQfaUvMUPoYox6c9hTMVct0OuN89mG-xE134m4ypAkcc-EZaS9ET-3wttjCCy0pPt13jOVl3N57eJHA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution"},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization"},{"id":46254,"name":"Optimization Problem","url":"https://www.academia.edu/Documents/in/Optimization_Problem"},{"id":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":74778,"name":"Crossover","url":"https://www.academia.edu/Documents/in/Crossover"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":1032348,"name":"Pareto Optimality","url":"https://www.academia.edu/Documents/in/Pareto_Optimality"},{"id":3924606,"name":"Test Suite","url":"https://www.academia.edu/Documents/in/Test_Suite"}],"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="118935199"><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/118935199/Threshold_Based_Penalty_Functions_for_Constrained_Multiobjective_Optimization"><img alt="Research paper thumbnail of Threshold Based Penalty Functions for Constrained Multiobjective Optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/114441300/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/118935199/Threshold_Based_Penalty_Functions_for_Constrained_Multiobjective_Optimization">Threshold Based Penalty Functions for Constrained Multiobjective Optimization</a></div><div class="wp-workCard_item"><span>International Journal of Advanced Computer Science and Applications</span><span>, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e548188f7e8a5254a603dbd2789978e6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":114441300,"asset_id":118935199,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/114441300/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="118935199"><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="118935199"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118935199; 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These penalty functions are incorporated in the update and replacement scheme of the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to solve constrained multiobjective optimization problems (CMOPs). As a result, the capability of MOEA/D is extended to handle constraints, and a new algorithm, denoted by CMOEA/D-DE-TDA is proposed. The performance of CMOEA/D-DE-TDA is tested, in terms of the values of IGDmetric and SC-metric, on the well known CF-series test instances. The experimental results are also compared with the three best performers of CEC 2009 MOEA competition. Empirical results show the pitfalls of the proposed penalty functions.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"International Journal of Advanced Computer Science and Applications","grobid_abstract_attachment_id":114441300},"translated_abstract":null,"internal_url":"https://www.academia.edu/118935199/Threshold_Based_Penalty_Functions_for_Constrained_Multiobjective_Optimization","translated_internal_url":"","created_at":"2024-05-11T22:25:57.353-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":114441300,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441300/thumbnails/1.jpg","file_name":"7fd7d76995a32943b8c1377807cc8c870f03.pdf","download_url":"https://www.academia.edu/attachments/114441300/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Threshold_Based_Penalty_Functions_for_Co.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441300/7fd7d76995a32943b8c1377807cc8c870f03-libre.pdf?1715493004=\u0026response-content-disposition=attachment%3B+filename%3DThreshold_Based_Penalty_Functions_for_Co.pdf\u0026Expires=1732704026\u0026Signature=c4C7bmgUm0dSNKXpo0JGDtIRwpURK9-V8WPqszG4-4zYoP0DRPFhraygnm~IlMrN3LhctxbDT~4eMHwB22wgxccPC-1LRvUbh46E9yOesyOJUCi1yvzz7IN2riil3R4XXy58heR1hHVa43-Fl~541G4Si59Nuge~Os-~AYLi70WF626NDRb8BvHDLLByPCGcjD99x1pQbLoo~Y73URMWvX3N0AiLRFnrHzcXmsXPH8-P8G-7-QKRozw-tMo4tAN5TYUl~SV9DlFS0aIBPf-AmR5dl14D8nAuf~CkaMYIwG6MOaJM~mbtrFaxvTHQDq8NVvcMlrtaErZbW1X6Mpw67g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Threshold_Based_Penalty_Functions_for_Constrained_Multiobjective_Optimization","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[{"id":114441300,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441300/thumbnails/1.jpg","file_name":"7fd7d76995a32943b8c1377807cc8c870f03.pdf","download_url":"https://www.academia.edu/attachments/114441300/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Threshold_Based_Penalty_Functions_for_Co.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441300/7fd7d76995a32943b8c1377807cc8c870f03-libre.pdf?1715493004=\u0026response-content-disposition=attachment%3B+filename%3DThreshold_Based_Penalty_Functions_for_Co.pdf\u0026Expires=1732704026\u0026Signature=c4C7bmgUm0dSNKXpo0JGDtIRwpURK9-V8WPqszG4-4zYoP0DRPFhraygnm~IlMrN3LhctxbDT~4eMHwB22wgxccPC-1LRvUbh46E9yOesyOJUCi1yvzz7IN2riil3R4XXy58heR1hHVa43-Fl~541G4Si59Nuge~Os-~AYLi70WF626NDRb8BvHDLLByPCGcjD99x1pQbLoo~Y73URMWvX3N0AiLRFnrHzcXmsXPH8-P8G-7-QKRozw-tMo4tAN5TYUl~SV9DlFS0aIBPf-AmR5dl14D8nAuf~CkaMYIwG6MOaJM~mbtrFaxvTHQDq8NVvcMlrtaErZbW1X6Mpw67g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":6414,"name":"Decomposition","url":"https://www.academia.edu/Documents/in/Decomposition"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"}],"urls":[]}, dispatcherData: dispatcherData }); 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We aim 3 to minimize the overall energy consumption of the system by 4 planning the trajectories of UAVs. To plan the trajectories of 5 UAVs, we need to consider the deployment of hovering points 6 (HPs) of UAVs, their association with UAVs, and their order for 7 each UAV. Therefore, the problem is very complicated, as it is 8 non-convex, nonlinear, NP-hard, and mixed-integer. To solve the 9 problem, this paper proposed an evolutionary trajectory planning 10 algorithm (ETPA), which comprises three phases. In the first 11 phase, variable-length GA is adopted to update the deployments 12 of HPs for UAVs. Accordingly, redundant HPs are removed by the 13 remove operator. Subsequently, differential evolution clustering is 14 adopted to cluster HPs into different clusters without knowing the 15 number of HPs in advance. Finally, a GA is proposed to construct 16 the order of HPs for UAVs. The experimental results on a set of 17 eight instances show that the proposed ETPA outperforms other 18 compared algorithms in terms of the energy consumption of the 19 system. 20 Index Terms-Mobile edge computing, unmanned aerial vehi-21 cle, evolutionary algorithm, multi-chrome genetic algorithm. 22 I. INTRODUCTION 23 With the development of mobile communication systems, 24 a huge number of resource-intensive and latency-sensitive ap-25 plications are emerging, such as virtual reality, online gaming, 26 and so on. Such applications are usually sensitive to latency 27 and require huge computational resources. however, due to 28 limitations on mobile users (MUs) devices, it is very difficult 29 to execute these tasks on them. 30 Mobile edge computing (MEC) is a promising technology 31 to address the above-mentioned issue. It can provide service 32 with low latency and high reliability near or at MUs. It can 33 execute tasks of MUs at the nearby edge cloud and sends back 34 the results to MUs [1]. Due to the shorter physical distance 35 between MEC's server/edge cloud and MUs, it consumes less 36 energy as compared to mobile cloud computing. However, it is 37 still lacking in fulfilling the requirements of MUs, as the loca-38 tion of the edge cloud is usually fixed and cannot be adjusted 39","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Soft Computing","grobid_abstract_attachment_id":110896497},"translated_abstract":null,"internal_url":"https://www.academia.edu/114112391/An_evolutionary_trajectory_planning_algorithm_for_multi_UAV_assisted_MEC_system","translated_internal_url":"","created_at":"2024-01-27T06:42:18.164-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":110896497,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/110896497/thumbnails/1.jpg","file_name":"v1.pdf","download_url":"https://www.academia.edu/attachments/110896497/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_evolutionary_trajectory_planning_algo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/110896497/v1-libre.pdf?1706368408=\u0026response-content-disposition=attachment%3B+filename%3DAn_evolutionary_trajectory_planning_algo.pdf\u0026Expires=1732704026\u0026Signature=bXMTVXJTu5wLyfvbUx-aCTrh4g2C-~OKR4mTHY~FZL0Al3XzkSiQTgkm8y5Jq0T96iChP7UVXfTP7GKcQtD44bR51iEeVOc-XaY0vplfTlNAKm3GZt71s5--tF8YRzH9zoIq-lniXfze4fIHcmJsuvKlLxXkqPAACvPWbBOkoPiYvHK9y03qyC5hojplxbrlnezwOCGzmVfWYYTmM0HwI4NBrdKB-FyHAYfF4XRPJA2sTLjbMtJiEomoYlti1uEHhvlvdDJwuV9DYfPYCldyBEItM6ZKpBsnA3JQptIxK18CFTzPSjTvezUjQ4jl01tuyWESpPriLBcmMx4GAtD3dg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_evolutionary_trajectory_planning_algorithm_for_multi_UAV_assisted_MEC_system","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[{"id":110896497,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/110896497/thumbnails/1.jpg","file_name":"v1.pdf","download_url":"https://www.academia.edu/attachments/110896497/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_evolutionary_trajectory_planning_algo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/110896497/v1-libre.pdf?1706368408=\u0026response-content-disposition=attachment%3B+filename%3DAn_evolutionary_trajectory_planning_algo.pdf\u0026Expires=1732704026\u0026Signature=bXMTVXJTu5wLyfvbUx-aCTrh4g2C-~OKR4mTHY~FZL0Al3XzkSiQTgkm8y5Jq0T96iChP7UVXfTP7GKcQtD44bR51iEeVOc-XaY0vplfTlNAKm3GZt71s5--tF8YRzH9zoIq-lniXfze4fIHcmJsuvKlLxXkqPAACvPWbBOkoPiYvHK9y03qyC5hojplxbrlnezwOCGzmVfWYYTmM0HwI4NBrdKB-FyHAYfF4XRPJA2sTLjbMtJiEomoYlti1uEHhvlvdDJwuV9DYfPYCldyBEItM6ZKpBsnA3JQptIxK18CFTzPSjTvezUjQ4jl01tuyWESpPriLBcmMx4GAtD3dg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":6132,"name":"Soft Computing","url":"https://www.academia.edu/Documents/in/Soft_Computing"},{"id":11397,"name":"Energy Consumption","url":"https://www.academia.edu/Documents/in/Energy_Consumption"},{"id":59770,"name":"Trajectory","url":"https://www.academia.edu/Documents/in/Trajectory"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm"},{"id":1268642,"name":"Software Deployment","url":"https://www.academia.edu/Documents/in/Software_Deployment"}],"urls":[{"id":38963500,"url":"https://link.springer.com/content/pdf/10.1007/s00500-021-06465-y.pdf"}]}, dispatcherData: dispatcherData }); 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It is population based, nature-inspired, and meta-heuristic that imitates teaching learning process. It has two phases, teacher and learner. In teacher phase, the teacher who is well-learned person transfers his/her knowledge to the learners to raise their grades/results; while in learner phase, learners/pupils learn and refine their knowledge through mutual interconnection. To solve constrained optimization problems (COPs) through TLBO we need to merge it with some constraint handling technique (CHT). Superiority of feasibility (SF) is a concept for making CHTs, existed in different forms based on various decisive factors. Most commonly used decision making factors in SF are number of constraints violated (NCV) and weighted mean (WM) values for comparing solutions. In this work, SF based on number of constraints violated (NCVSF) and weighted mean (...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="46df02be9d9a151c36b4552cb3892d80" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":110896496,"asset_id":114112390,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/110896496/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="114112390"><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="114112390"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114112390; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114112390]").text(description); $(".js-view-count[data-work-id=114112390]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114112390; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114112390']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 114112390, 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: "46df02be9d9a151c36b4552cb3892d80" } } $('.js-work-strip[data-work-id=114112390]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114112390,"title":"Constrained Optimization Based on Hybridized Version of Superiority of Feasibility Solution Strategy","translated_title":"","metadata":{"abstract":"Teaching learning based optimization (TLBO) is a stochastic algorithm which was first proposed for unconstrained optimization problems. It is population based, nature-inspired, and meta-heuristic that imitates teaching learning process. It has two phases, teacher and learner. In teacher phase, the teacher who is well-learned person transfers his/her knowledge to the learners to raise their grades/results; while in learner phase, learners/pupils learn and refine their knowledge through mutual interconnection. To solve constrained optimization problems (COPs) through TLBO we need to merge it with some constraint handling technique (CHT). Superiority of feasibility (SF) is a concept for making CHTs, existed in different forms based on various decisive factors. Most commonly used decision making factors in SF are number of constraints violated (NCV) and weighted mean (WM) values for comparing solutions. In this work, SF based on number of constraints violated (NCVSF) and weighted mean (...","publisher":"Research Square Platform LLC","publication_date":{"day":null,"month":null,"year":2021,"errors":{}}},"translated_abstract":"Teaching learning based optimization (TLBO) is a stochastic algorithm which was first proposed for unconstrained optimization problems. It is population based, nature-inspired, and meta-heuristic that imitates teaching learning process. It has two phases, teacher and learner. In teacher phase, the teacher who is well-learned person transfers his/her knowledge to the learners to raise their grades/results; while in learner phase, learners/pupils learn and refine their knowledge through mutual interconnection. To solve constrained optimization problems (COPs) through TLBO we need to merge it with some constraint handling technique (CHT). Superiority of feasibility (SF) is a concept for making CHTs, existed in different forms based on various decisive factors. Most commonly used decision making factors in SF are number of constraints violated (NCV) and weighted mean (WM) values for comparing solutions. 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Various topological indices, including the atom-bond connectivity index, the geometric–arithmetic index, and the Randić index, can be utilized to determine various characteristics, such as physicochemical activity, chemical activity, and thermodynamic properties. Meanwhile, the non-commuting graph ΓG of a finite group G is a graph where non-central elements of G are its vertex set, while two different elements are edge connected when they do not commute in G. In this article, we investigate several topological properties of non-commuting graphs of finite groups, such as the Harary index, the harmonic index, the Randić index, reciprocal Wiener index, atomic-bond connectivity index, and the geometric–arithmetic index. In addition, we analyze the Hosoya characteristics, such as the Hosoya polynomial and the reciprocal sta...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08891266cec88a571c4def18e0cc9663" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":110896479,"asset_id":114112389,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/110896479/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="114112389"><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="114112389"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114112389; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114112389]").text(description); $(".js-view-count[data-work-id=114112389]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114112389; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114112389']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 114112389, 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: "08891266cec88a571c4def18e0cc9663" } } $('.js-work-strip[data-work-id=114112389]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114112389,"title":"Certain Topological Indices of Non-Commuting Graphs for Finite Non-Abelian Groups","translated_title":"","metadata":{"abstract":"A topological index is a number derived from a molecular structure (i.e., a graph) that represents the fundamental structural characteristics of a suggested molecule. 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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="104821566"><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/104821566/Q_Rung_Orthopair_Fuzzy_2_TUPLE_Linguistic_Clustering_Algorithm_and_Its_Applications_to_Clustering_Analysis"><img alt="Research paper thumbnail of Q-Rung Orthopair Fuzzy 2-TUPLE Linguistic Clustering Algorithm and Its Applications to Clustering Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/104448142/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/104821566/Q_Rung_Orthopair_Fuzzy_2_TUPLE_Linguistic_Clustering_Algorithm_and_Its_Applications_to_Clustering_Analysis">Q-Rung Orthopair Fuzzy 2-TUPLE Linguistic Clustering Algorithm and Its Applications to Clustering Analysis</a></div><div class="wp-workCard_item"><span>Scientific Reports</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">q-ROPFLS, including numeric and linguistic data, has a wide range of applications in handling unc...</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">q-ROPFLS, including numeric and linguistic data, has a wide range of applications in handling uncertain information. This article aims to investigate q-ROPFL correlation coefficient based on the proposed information energy and covariance formulas. Moreover, considering that different q-ROPFL elements may have varying criteria weights, the weighted correlation coefficient is further explored. Some desirable characteristics of the presented correlation coefficients are also discussed and proven. In addition, some theoretical development is provided, including the concept of composition matrix, correlation matrix, and equivalent correlation matrix via the proposed correlation coefficients. Then, a clustering algorithm is expanded where data is expressed in q-ROPFL form with unknown weight information and is explained through an illustrative example. Besides, detailed parameter analysis and comparative study are performed with the existing approaches to reveal the effectiveness of the f...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c68e06f526062ab5a8e16808f9367205" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448142,"asset_id":104821566,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448142/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="104821566"><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="104821566"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821566; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104821566]").text(description); $(".js-view-count[data-work-id=104821566]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104821566; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104821566']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104821566, 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: "c68e06f526062ab5a8e16808f9367205" } } $('.js-work-strip[data-work-id=104821566]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104821566,"title":"Q-Rung Orthopair Fuzzy 2-TUPLE Linguistic Clustering Algorithm and Its Applications to Clustering Analysis","translated_title":"","metadata":{"abstract":"q-ROPFLS, including numeric and linguistic data, has a wide range of applications in handling uncertain information. 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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="104821565"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/104821565/Extension_of_GRA_method_for_multiattribute_group_decision_making_problem_under_linguistic_Pythagorean_fuzzy_setting_with_incomplete_weight_information"><img alt="Research paper thumbnail of Extension of GRA method for multiattribute group decision making problem under linguistic Pythagorean fuzzy setting with incomplete weight information" 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" rel="nofollow" href="https://www.academia.edu/104821565/Extension_of_GRA_method_for_multiattribute_group_decision_making_problem_under_linguistic_Pythagorean_fuzzy_setting_with_incomplete_weight_information">Extension of GRA method for multiattribute group decision making problem under linguistic Pythagorean fuzzy setting with incomplete weight information</a></div><div class="wp-workCard_item"><span>International Journal of Intelligent Systems</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="104821565"><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="104821565"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821565; 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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="104821564"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/104821564/Hybrid_Honey_Bees_Meta_Heuristic_for_Benchmark_Data_Classification"><img alt="Research paper thumbnail of Hybrid Honey Bees Meta-Heuristic for Benchmark Data Classification" 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" rel="nofollow" href="https://www.academia.edu/104821564/Hybrid_Honey_Bees_Meta_Heuristic_for_Benchmark_Data_Classification">Hybrid Honey Bees Meta-Heuristic for Benchmark Data Classification</a></div><div class="wp-workCard_item"><span>Exploring Critical Approaches of Evolutionary Computation</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorith...</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">Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorithms. Sometimes, these algorithms cannot reach to global optima due to randomization and poor exploration and exploitation process. Here, the global artificial bee colony and Levenberq-Marquardt hybrid called GABC-LM algorithm is proposed. The proposed GABC-LM will use neural network for obtaining the accurate parameters, weights, and bias values for benchmark dataset classification. The performance of GABC-LM is benchmarked against NNs training with the typical LM, PSO, ABC, and GABC methods. The experimental result shows that the proposed GABC-LM performs better than that standard BP, ABC, PSO, and GABC for the classification task.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="104821564"><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="104821564"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821564; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104821564]").text(description); $(".js-view-count[data-work-id=104821564]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104821564; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104821564']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104821564, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=104821564]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104821564,"title":"Hybrid Honey Bees Meta-Heuristic for Benchmark Data Classification","translated_title":"","metadata":{"abstract":"Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorithms. Sometimes, these algorithms cannot reach to global optima due to randomization and poor exploration and exploitation process. Here, the global artificial bee colony and Levenberq-Marquardt hybrid called GABC-LM algorithm is proposed. The proposed GABC-LM will use neural network for obtaining the accurate parameters, weights, and bias values for benchmark dataset classification. The performance of GABC-LM is benchmarked against NNs training with the typical LM, PSO, ABC, and GABC methods. The experimental result shows that the proposed GABC-LM performs better than that standard BP, ABC, PSO, and GABC for the classification task.","publisher":"IGI Global","publication_name":"Exploring Critical Approaches of Evolutionary Computation"},"translated_abstract":"Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorithms. Sometimes, these algorithms cannot reach to global optima due to randomization and poor exploration and exploitation process. Here, the global artificial bee colony and Levenberq-Marquardt hybrid called GABC-LM algorithm is proposed. The proposed GABC-LM will use neural network for obtaining the accurate parameters, weights, and bias values for benchmark dataset classification. The performance of GABC-LM is benchmarked against NNs training with the typical LM, PSO, ABC, and GABC methods. The experimental result shows that the proposed GABC-LM performs better than that standard BP, ABC, PSO, and GABC for the classification task.","internal_url":"https://www.academia.edu/104821564/Hybrid_Honey_Bees_Meta_Heuristic_for_Benchmark_Data_Classification","translated_internal_url":"","created_at":"2023-07-22T08:36:31.240-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Hybrid_Honey_Bees_Meta_Heuristic_for_Benchmark_Data_Classification","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"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":131956,"name":"Cuckoo Search","url":"https://www.academia.edu/Documents/in/Cuckoo_Search"},{"id":586207,"name":"IGI Global","url":"https://www.academia.edu/Documents/in/IGI_Global"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":33020015,"url":"https://www.igi-global.com/viewtitle.aspx?TitleId=208046"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="104821563"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/104821563/Soft_computing_techniques_for_forecasting_of_COVID_19_in_Pakistan"><img alt="Research paper thumbnail of Soft computing techniques for forecasting of COVID-19 in Pakistan" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/104821563/Soft_computing_techniques_for_forecasting_of_COVID_19_in_Pakistan">Soft computing techniques for forecasting of COVID-19 in Pakistan</a></div><div class="wp-workCard_item"><span>Alexandria Engineering Journal</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104821563"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104821563"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821563; 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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="104821562"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/104821562/Energy_Consumption_and_Sustainable_Services_in_Intelligent_Reflecting_Surface_and_Unmanned_Aerial_Vehicles_Assisted_MEC_System_for_Large_Scale_Internet_of_Things_Devices"><img alt="Research paper thumbnail of Energy Consumption and Sustainable Services in Intelligent Reflecting Surface and Unmanned Aerial Vehicles-Assisted MEC System for Large-Scale Internet of Things Devices" 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" rel="nofollow" href="https://www.academia.edu/104821562/Energy_Consumption_and_Sustainable_Services_in_Intelligent_Reflecting_Surface_and_Unmanned_Aerial_Vehicles_Assisted_MEC_System_for_Large_Scale_Internet_of_Things_Devices">Energy Consumption and Sustainable Services in Intelligent Reflecting Surface and Unmanned Aerial Vehicles-Assisted MEC System for Large-Scale Internet of Things Devices</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Green Communications and Networking</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="104821562"><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="104821562"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821562; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104821562]").text(description); $(".js-view-count[data-work-id=104821562]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104821562; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104821562']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104821562, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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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="104821561"><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/104821561/An_Advanced_Amalgam_of_Nature_Inspired_Algorithms_for_Global_Optimization_Problems"><img alt="Research paper thumbnail of An Advanced Amalgam of Nature-Inspired Algorithms for Global Optimization Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/104448138/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/104821561/An_Advanced_Amalgam_of_Nature_Inspired_Algorithms_for_Global_Optimization_Problems">An Advanced Amalgam of Nature-Inspired Algorithms for Global Optimization Problems</a></div><div class="wp-workCard_item"><span>Mathematical Problems in Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Large-scale global optimization problems are ambitious and quite difficult to handle with determi...</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">Large-scale global optimization problems are ambitious and quite difficult to handle with deterministic methods. The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. In this paper, an advanced amalgam of nature-inspired algorithms (ANIA) is developed by employing GWO and TLBO as constituent algorithms. Initially, an equal number of solutions are assigned to both NIAs to perform their search process of ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="68f40791b50c7afc65efc71798acf1b0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448138,"asset_id":104821561,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448138/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="104821561"><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="104821561"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821561; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104821561]").text(description); $(".js-view-count[data-work-id=104821561]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104821561; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104821561']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104821561, 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: "68f40791b50c7afc65efc71798acf1b0" } } $('.js-work-strip[data-work-id=104821561]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104821561,"title":"An Advanced Amalgam of Nature-Inspired Algorithms for Global Optimization Problems","translated_title":"","metadata":{"abstract":"Large-scale global optimization problems are ambitious and quite difficult to handle with deterministic methods. The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. In this paper, an advanced amalgam of nature-inspired algorithms (ANIA) is developed by employing GWO and TLBO as constituent algorithms. Initially, an equal number of solutions are assigned to both NIAs to perform their search process of ...","publisher":"Hindawi Limited","publication_name":"Mathematical Problems in Engineering"},"translated_abstract":"Large-scale global optimization problems are ambitious and quite difficult to handle with deterministic methods. The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. 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Continua</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9266ef27506d16b314eacb81526ffb44" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448164,"asset_id":104821560,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448164/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="104821560"><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="104821560"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821560; 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Grasshopper optimization algorithm (GOA) is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature, which has good convergence ability towards optima. The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS. The antecedent part parameters (Gaussian membership function parameters) are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm. Tuning of the consequent part parameters are accomplished using extreme learning machine. The optimized IT2-FLS (GOAIT2FELM) obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices. The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm. 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Evolutionary algorithms are not directly applied on constrained optimization problems. However, different constraint-handling techniques are incorporated in their framework to adopt it for dealing with constrained environments. This paper suggests an hybrid constrained evolutionary algorithm (HCEA) that employs two penalty functions simultaneously. The suggested HCEA has two versions namely HCEA-static and HCEA-adaptive. The performance of the HCEA-static and HCEA-adaptive algorithms are examined upon the constrained benchmark functions that are recently designed for the special session of the 2006 IEEE Conference of Evolutionary Computation (IEEE-CEC&#39;06). The experimental results of the suggested algorithms are much promising as compared to one of the recent constrained version of the JADE. The converging behaviour of the both...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d44b042641566e887e8c0161fce836e9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448163,"asset_id":104821556,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448163/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="104821556"><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="104821556"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821556; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104821556]").text(description); $(".js-view-count[data-work-id=104821556]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104821556; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104821556']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104821556, 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: "d44b042641566e887e8c0161fce836e9" } } $('.js-work-strip[data-work-id=104821556]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104821556,"title":"Hybrid Constrained Evolutionary Algorithm For Numerical Optimization Problems","translated_title":"","metadata":{"abstract":"Constrained optimization are naturally arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers. 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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="12427519" id="papers"><div class="js-work-strip profile--work_container" data-work-id="118935219"><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/118935219/The_State_of_AI_Empowered_Backscatter_Communications_A_Comprehensive_Survey"><img alt="Research paper thumbnail of The State of AI-Empowered Backscatter Communications: A Comprehensive Survey" class="work-thumbnail" src="https://attachments.academia-assets.com/114441307/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/118935219/The_State_of_AI_Empowered_Backscatter_Communications_A_Comprehensive_Survey">The State of AI-Empowered Backscatter Communications: A Comprehensive Survey</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper brings these two technologies together to investigate the current state of AI-powered ...</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 paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with RIS, UAV, and vehicular networks. Finally, we highlight the challenges and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fd00ac8022a24aabce7f18dc0fa56da5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":114441307,"asset_id":118935219,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/114441307/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="118935219"><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="118935219"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118935219; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118935219]").text(description); $(".js-view-count[data-work-id=118935219]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118935219; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118935219']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118935219, 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: "fd00ac8022a24aabce7f18dc0fa56da5" } } $('.js-work-strip[data-work-id=118935219]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118935219,"title":"The State of AI-Empowered Backscatter Communications: A Comprehensive Survey","translated_title":"","metadata":{"abstract":"This paper brings these two technologies together to investigate the current state of AI-powered BC. 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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="118935217"><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/118935217/Applications_of_q_Derivative_Operator_to_the_Subclass_of_Bi_Univalent_Functions_Involving_q_Chebyshev_Polynomials"><img alt="Research paper thumbnail of Applications of q -Derivative Operator to the Subclass of Bi-Univalent Functions Involving q -Chebyshev Polynomials" class="work-thumbnail" src="https://attachments.academia-assets.com/114441304/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/118935217/Applications_of_q_Derivative_Operator_to_the_Subclass_of_Bi_Univalent_Functions_Involving_q_Chebyshev_Polynomials">Applications of q -Derivative Operator to the Subclass of Bi-Univalent Functions Involving q -Chebyshev Polynomials</a></div><div class="wp-workCard_item"><span>Journal of Mathematics</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In recent years, the usage of the q -derivative and symmetric q -derivative operators is signific...</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">In recent years, the usage of the q -derivative and symmetric q -derivative operators is significant. In this study, firstly, many known concepts of the q -derivative operator are highlighted and given. We then use the symmetric q -derivative operator and certain q -Chebyshev polynomials to define a new subclass of analytic and bi-univalent functions. For this newly defined functions’ classes, a number of coefficient bounds, along with the Fekete–Szegö inequalities, are also given. To validate our results, we give some known consequences in form of remarks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="16c9b2d33eb917004d49523e96912462" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":114441304,"asset_id":118935217,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/114441304/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="118935217"><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="118935217"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118935217; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118935217]").text(description); $(".js-view-count[data-work-id=118935217]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118935217; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118935217']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118935217, 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: "16c9b2d33eb917004d49523e96912462" } } $('.js-work-strip[data-work-id=118935217]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118935217,"title":"Applications of q -Derivative Operator to the Subclass of Bi-Univalent Functions Involving q -Chebyshev Polynomials","translated_title":"","metadata":{"abstract":"In recent years, the usage of the q -derivative and symmetric q -derivative operators is significant. 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For this newly defined functions’ classes, a number of coefficient bounds, along with the Fekete–Szegö inequalities, are also given. 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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="118935216"><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/118935216/Reflected_Adaptive_Differential_Evolution_with_Two_External_Archives_for_Large_Scale_Global_Optimization"><img alt="Research paper thumbnail of Reflected Adaptive Differential Evolution with Two External Archives for Large-Scale Global Optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/114441320/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/118935216/Reflected_Adaptive_Differential_Evolution_with_Two_External_Archives_for_Large_Scale_Global_Optimization">Reflected Adaptive Differential Evolution with Two External Archives for Large-Scale Global Optimization</a></div><div class="wp-workCard_item"><span>International Journal of Advanced Computer Science and Applications</span><span>, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d707c7f78b5568b7ff9ad09dcb5448a9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":114441320,"asset_id":118935216,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/114441320/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="118935216"><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="118935216"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118935216; 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It performed considerably improved on a set of well-studied benchmark test problems. In this paper, we evaluate the performance of new JADE with two external archives to deal with unconstrained continuous large-scale global optimization problems labeled as Reflected Adaptive Differential Evolution with Two External Archives (RJADE/TA). The only archive of JADE stores failed solutions. In contrast, the proposed second archive stores superior solutions at regular intervals of the optimization process to avoid premature convergence towards local optima. The superior solutions which are sent to the archive are reflected by new potential solutions. At the end of the search process, the best solution is selected from the second archive and the current population. The performance of RJADE/TA algorithm is then extensively evaluated on two test beds. At first on 28 latest benchmark functions constructed for the 2013 Congress on Evolutionary Computation special session. Secondly on ten benchmark problems from CEC2010 Special Session and Competition on Large-Scale Global Optimization. Experimental results demonstrated a very competitive performance of the algorithm.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"International Journal of Advanced Computer Science and Applications","grobid_abstract_attachment_id":114441320},"translated_abstract":null,"internal_url":"https://www.academia.edu/118935216/Reflected_Adaptive_Differential_Evolution_with_Two_External_Archives_for_Large_Scale_Global_Optimization","translated_internal_url":"","created_at":"2024-05-11T22:27:13.094-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":114441320,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441320/thumbnails/1.jpg","file_name":"ed49b451b456d480afaf09e0c5f1d7bace85.pdf","download_url":"https://www.academia.edu/attachments/114441320/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Reflected_Adaptive_Differential_Evolutio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441320/ed49b451b456d480afaf09e0c5f1d7bace85-libre.pdf?1715492473=\u0026response-content-disposition=attachment%3B+filename%3DReflected_Adaptive_Differential_Evolutio.pdf\u0026Expires=1732704026\u0026Signature=Avza7p7UjzIO-8JnYIfwFsr~SFrkLobr1uGvcadzGyrCjBBs1tKDtjt7IAAaBT7l43y03MSU7d~vNYo5IUnojul7hM78KNdTf8ELkA2b1JjMxOcB6KITStrO2FMa6DT8HVzuwRAOBJCy6UiTImOV1nv-RDoTEZZsyCxLKPPF2yZ9X97yTKHGbXaZoZcY-M8S9Y-OmzArQzRMYWBW5Lyvh62iCPLgVRJ7wgyrviTdNCV5BKEbefU3pNfs0GgTYzLHrWSz0VakvZ4uQR1706IcQNTzkigNxLVuo1IDahR2bVkh7rqq59h~G01C~4opV~libf00i5gPKOZo5G3gdr4uJQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Reflected_Adaptive_Differential_Evolution_with_Two_External_Archives_for_Large_Scale_Global_Optimization","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[{"id":114441320,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441320/thumbnails/1.jpg","file_name":"ed49b451b456d480afaf09e0c5f1d7bace85.pdf","download_url":"https://www.academia.edu/attachments/114441320/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Reflected_Adaptive_Differential_Evolutio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441320/ed49b451b456d480afaf09e0c5f1d7bace85-libre.pdf?1715492473=\u0026response-content-disposition=attachment%3B+filename%3DReflected_Adaptive_Differential_Evolutio.pdf\u0026Expires=1732704026\u0026Signature=Avza7p7UjzIO-8JnYIfwFsr~SFrkLobr1uGvcadzGyrCjBBs1tKDtjt7IAAaBT7l43y03MSU7d~vNYo5IUnojul7hM78KNdTf8ELkA2b1JjMxOcB6KITStrO2FMa6DT8HVzuwRAOBJCy6UiTImOV1nv-RDoTEZZsyCxLKPPF2yZ9X97yTKHGbXaZoZcY-M8S9Y-OmzArQzRMYWBW5Lyvh62iCPLgVRJ7wgyrviTdNCV5BKEbefU3pNfs0GgTYzLHrWSz0VakvZ4uQR1706IcQNTzkigNxLVuo1IDahR2bVkh7rqq59h~G01C~4opV~libf00i5gPKOZo5G3gdr4uJQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation"},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution"},{"id":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":107131,"name":"Global Optimization","url":"https://www.academia.edu/Documents/in/Global_Optimization"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_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="118935215"><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/118935215/Enhanced_Version_of_Multi_algorithm_Genetically_Adaptive_for_Multiobjective_optimization"><img alt="Research paper thumbnail of Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/114441322/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/118935215/Enhanced_Version_of_Multi_algorithm_Genetically_Adaptive_for_Multiobjective_optimization">Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization</a></div><div class="wp-workCard_item"><span>International Journal of Advanced Computer Science and Applications</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f4ffa574a84228277bed6016a9dc521f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":114441322,"asset_id":118935215,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/114441322/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="118935215"><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="118935215"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118935215; 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MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different problems. The use of multiple operators with a selfadaptive capability can further improve the performance of existing MOEAs. This paper suggests an enhanced version of a genetically adaptive multi-algorithm for multi-objective (AMAL-GAM) optimisation which includes differential evolution (DE), particle swarm optimization (PSO), simulated binary crossover (SBX), Pareto archive evolution strategy (PAES) and simplex crossover (SPX) for population evolution during the course of optimization. We examine the performance of this enhanced version of AMALGAM experimentally over two different test suites, the ZDT test problems and the test instances designed recently for the special session on MOEA's competition at the Congress of Evolutionary Computing of 2009 (CEC'09). The suggested algorithm has found better approximate solutions on most test problems in terms of inverted generational distance (IGD) as the metric indicator.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"International Journal of Advanced Computer Science and Applications","grobid_abstract_attachment_id":114441322},"translated_abstract":null,"internal_url":"https://www.academia.edu/118935215/Enhanced_Version_of_Multi_algorithm_Genetically_Adaptive_for_Multiobjective_optimization","translated_internal_url":"","created_at":"2024-05-11T22:27:12.565-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":114441322,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441322/thumbnails/1.jpg","file_name":"084a466120acb26f1ce748f3ea27b931cd90.pdf","download_url":"https://www.academia.edu/attachments/114441322/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Enhanced_Version_of_Multi_algorithm_Gene.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441322/084a466120acb26f1ce748f3ea27b931cd90-libre.pdf?1715492866=\u0026response-content-disposition=attachment%3B+filename%3DEnhanced_Version_of_Multi_algorithm_Gene.pdf\u0026Expires=1732704026\u0026Signature=BYTkvlMadIdeGbkgAZYEaRL2rNAdZBXfiZJgiR6Z08UO-qGAcKqmjrdcGR4xzW6Pl6HraLMO7Bf4NuFy575DkQlzy1npRstZRlciAVzvCe~rQnqhhwYwLHJr8M6XGyYLkALUW3UCH2x-W-W5lU4hpFo8dwZwqTfpIsLfjVBwsXdHNl0Qh~89e3n2p0GDcEbvjMiHQ4kBrRJzjHPCbA0fL5sVRi68xdRpfPfqySp17FGvmWd2escTwUrOS2CfBpFdhOLM9ZOcQfaUvMUPoYox6c9hTMVct0OuN89mG-xE134m4ypAkcc-EZaS9ET-3wttjCCy0pPt13jOVl3N57eJHA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Enhanced_Version_of_Multi_algorithm_Genetically_Adaptive_for_Multiobjective_optimization","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[{"id":114441322,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441322/thumbnails/1.jpg","file_name":"084a466120acb26f1ce748f3ea27b931cd90.pdf","download_url":"https://www.academia.edu/attachments/114441322/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Enhanced_Version_of_Multi_algorithm_Gene.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441322/084a466120acb26f1ce748f3ea27b931cd90-libre.pdf?1715492866=\u0026response-content-disposition=attachment%3B+filename%3DEnhanced_Version_of_Multi_algorithm_Gene.pdf\u0026Expires=1732704026\u0026Signature=BYTkvlMadIdeGbkgAZYEaRL2rNAdZBXfiZJgiR6Z08UO-qGAcKqmjrdcGR4xzW6Pl6HraLMO7Bf4NuFy575DkQlzy1npRstZRlciAVzvCe~rQnqhhwYwLHJr8M6XGyYLkALUW3UCH2x-W-W5lU4hpFo8dwZwqTfpIsLfjVBwsXdHNl0Qh~89e3n2p0GDcEbvjMiHQ4kBrRJzjHPCbA0fL5sVRi68xdRpfPfqySp17FGvmWd2escTwUrOS2CfBpFdhOLM9ZOcQfaUvMUPoYox6c9hTMVct0OuN89mG-xE134m4ypAkcc-EZaS9ET-3wttjCCy0pPt13jOVl3N57eJHA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution"},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization"},{"id":46254,"name":"Optimization Problem","url":"https://www.academia.edu/Documents/in/Optimization_Problem"},{"id":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":74778,"name":"Crossover","url":"https://www.academia.edu/Documents/in/Crossover"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":1032348,"name":"Pareto Optimality","url":"https://www.academia.edu/Documents/in/Pareto_Optimality"},{"id":3924606,"name":"Test Suite","url":"https://www.academia.edu/Documents/in/Test_Suite"}],"urls":[]}, dispatcherData: dispatcherData }); 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These penalty functions are incorporated in the update and replacement scheme of the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to solve constrained multiobjective optimization problems (CMOPs). As a result, the capability of MOEA/D is extended to handle constraints, and a new algorithm, denoted by CMOEA/D-DE-TDA is proposed. The performance of CMOEA/D-DE-TDA is tested, in terms of the values of IGDmetric and SC-metric, on the well known CF-series test instances. The experimental results are also compared with the three best performers of CEC 2009 MOEA competition. Empirical results show the pitfalls of the proposed penalty functions.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"International Journal of Advanced Computer Science and Applications","grobid_abstract_attachment_id":114441300},"translated_abstract":null,"internal_url":"https://www.academia.edu/118935199/Threshold_Based_Penalty_Functions_for_Constrained_Multiobjective_Optimization","translated_internal_url":"","created_at":"2024-05-11T22:25:57.353-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":114441300,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441300/thumbnails/1.jpg","file_name":"7fd7d76995a32943b8c1377807cc8c870f03.pdf","download_url":"https://www.academia.edu/attachments/114441300/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Threshold_Based_Penalty_Functions_for_Co.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441300/7fd7d76995a32943b8c1377807cc8c870f03-libre.pdf?1715493004=\u0026response-content-disposition=attachment%3B+filename%3DThreshold_Based_Penalty_Functions_for_Co.pdf\u0026Expires=1732704026\u0026Signature=c4C7bmgUm0dSNKXpo0JGDtIRwpURK9-V8WPqszG4-4zYoP0DRPFhraygnm~IlMrN3LhctxbDT~4eMHwB22wgxccPC-1LRvUbh46E9yOesyOJUCi1yvzz7IN2riil3R4XXy58heR1hHVa43-Fl~541G4Si59Nuge~Os-~AYLi70WF626NDRb8BvHDLLByPCGcjD99x1pQbLoo~Y73URMWvX3N0AiLRFnrHzcXmsXPH8-P8G-7-QKRozw-tMo4tAN5TYUl~SV9DlFS0aIBPf-AmR5dl14D8nAuf~CkaMYIwG6MOaJM~mbtrFaxvTHQDq8NVvcMlrtaErZbW1X6Mpw67g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Threshold_Based_Penalty_Functions_for_Constrained_Multiobjective_Optimization","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[{"id":114441300,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/114441300/thumbnails/1.jpg","file_name":"7fd7d76995a32943b8c1377807cc8c870f03.pdf","download_url":"https://www.academia.edu/attachments/114441300/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Threshold_Based_Penalty_Functions_for_Co.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/114441300/7fd7d76995a32943b8c1377807cc8c870f03-libre.pdf?1715493004=\u0026response-content-disposition=attachment%3B+filename%3DThreshold_Based_Penalty_Functions_for_Co.pdf\u0026Expires=1732704026\u0026Signature=c4C7bmgUm0dSNKXpo0JGDtIRwpURK9-V8WPqszG4-4zYoP0DRPFhraygnm~IlMrN3LhctxbDT~4eMHwB22wgxccPC-1LRvUbh46E9yOesyOJUCi1yvzz7IN2riil3R4XXy58heR1hHVa43-Fl~541G4Si59Nuge~Os-~AYLi70WF626NDRb8BvHDLLByPCGcjD99x1pQbLoo~Y73URMWvX3N0AiLRFnrHzcXmsXPH8-P8G-7-QKRozw-tMo4tAN5TYUl~SV9DlFS0aIBPf-AmR5dl14D8nAuf~CkaMYIwG6MOaJM~mbtrFaxvTHQDq8NVvcMlrtaErZbW1X6Mpw67g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":6414,"name":"Decomposition","url":"https://www.academia.edu/Documents/in/Decomposition"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"}],"urls":[]}, dispatcherData: dispatcherData }); 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We aim 3 to minimize the overall energy consumption of the system by 4 planning the trajectories of UAVs. To plan the trajectories of 5 UAVs, we need to consider the deployment of hovering points 6 (HPs) of UAVs, their association with UAVs, and their order for 7 each UAV. Therefore, the problem is very complicated, as it is 8 non-convex, nonlinear, NP-hard, and mixed-integer. To solve the 9 problem, this paper proposed an evolutionary trajectory planning 10 algorithm (ETPA), which comprises three phases. In the first 11 phase, variable-length GA is adopted to update the deployments 12 of HPs for UAVs. Accordingly, redundant HPs are removed by the 13 remove operator. Subsequently, differential evolution clustering is 14 adopted to cluster HPs into different clusters without knowing the 15 number of HPs in advance. Finally, a GA is proposed to construct 16 the order of HPs for UAVs. The experimental results on a set of 17 eight instances show that the proposed ETPA outperforms other 18 compared algorithms in terms of the energy consumption of the 19 system. 20 Index Terms-Mobile edge computing, unmanned aerial vehi-21 cle, evolutionary algorithm, multi-chrome genetic algorithm. 22 I. INTRODUCTION 23 With the development of mobile communication systems, 24 a huge number of resource-intensive and latency-sensitive ap-25 plications are emerging, such as virtual reality, online gaming, 26 and so on. Such applications are usually sensitive to latency 27 and require huge computational resources. however, due to 28 limitations on mobile users (MUs) devices, it is very difficult 29 to execute these tasks on them. 30 Mobile edge computing (MEC) is a promising technology 31 to address the above-mentioned issue. It can provide service 32 with low latency and high reliability near or at MUs. It can 33 execute tasks of MUs at the nearby edge cloud and sends back 34 the results to MUs [1]. Due to the shorter physical distance 35 between MEC's server/edge cloud and MUs, it consumes less 36 energy as compared to mobile cloud computing. However, it is 37 still lacking in fulfilling the requirements of MUs, as the loca-38 tion of the edge cloud is usually fixed and cannot be adjusted 39","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Soft Computing","grobid_abstract_attachment_id":110896497},"translated_abstract":null,"internal_url":"https://www.academia.edu/114112391/An_evolutionary_trajectory_planning_algorithm_for_multi_UAV_assisted_MEC_system","translated_internal_url":"","created_at":"2024-01-27T06:42:18.164-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":110896497,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/110896497/thumbnails/1.jpg","file_name":"v1.pdf","download_url":"https://www.academia.edu/attachments/110896497/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_evolutionary_trajectory_planning_algo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/110896497/v1-libre.pdf?1706368408=\u0026response-content-disposition=attachment%3B+filename%3DAn_evolutionary_trajectory_planning_algo.pdf\u0026Expires=1732704026\u0026Signature=bXMTVXJTu5wLyfvbUx-aCTrh4g2C-~OKR4mTHY~FZL0Al3XzkSiQTgkm8y5Jq0T96iChP7UVXfTP7GKcQtD44bR51iEeVOc-XaY0vplfTlNAKm3GZt71s5--tF8YRzH9zoIq-lniXfze4fIHcmJsuvKlLxXkqPAACvPWbBOkoPiYvHK9y03qyC5hojplxbrlnezwOCGzmVfWYYTmM0HwI4NBrdKB-FyHAYfF4XRPJA2sTLjbMtJiEomoYlti1uEHhvlvdDJwuV9DYfPYCldyBEItM6ZKpBsnA3JQptIxK18CFTzPSjTvezUjQ4jl01tuyWESpPriLBcmMx4GAtD3dg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_evolutionary_trajectory_planning_algorithm_for_multi_UAV_assisted_MEC_system","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[{"id":110896497,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/110896497/thumbnails/1.jpg","file_name":"v1.pdf","download_url":"https://www.academia.edu/attachments/110896497/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_evolutionary_trajectory_planning_algo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/110896497/v1-libre.pdf?1706368408=\u0026response-content-disposition=attachment%3B+filename%3DAn_evolutionary_trajectory_planning_algo.pdf\u0026Expires=1732704026\u0026Signature=bXMTVXJTu5wLyfvbUx-aCTrh4g2C-~OKR4mTHY~FZL0Al3XzkSiQTgkm8y5Jq0T96iChP7UVXfTP7GKcQtD44bR51iEeVOc-XaY0vplfTlNAKm3GZt71s5--tF8YRzH9zoIq-lniXfze4fIHcmJsuvKlLxXkqPAACvPWbBOkoPiYvHK9y03qyC5hojplxbrlnezwOCGzmVfWYYTmM0HwI4NBrdKB-FyHAYfF4XRPJA2sTLjbMtJiEomoYlti1uEHhvlvdDJwuV9DYfPYCldyBEItM6ZKpBsnA3JQptIxK18CFTzPSjTvezUjQ4jl01tuyWESpPriLBcmMx4GAtD3dg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":6132,"name":"Soft Computing","url":"https://www.academia.edu/Documents/in/Soft_Computing"},{"id":11397,"name":"Energy Consumption","url":"https://www.academia.edu/Documents/in/Energy_Consumption"},{"id":59770,"name":"Trajectory","url":"https://www.academia.edu/Documents/in/Trajectory"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm"},{"id":1268642,"name":"Software Deployment","url":"https://www.academia.edu/Documents/in/Software_Deployment"}],"urls":[{"id":38963500,"url":"https://link.springer.com/content/pdf/10.1007/s00500-021-06465-y.pdf"}]}, dispatcherData: dispatcherData }); 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It is population based, nature-inspired, and meta-heuristic that imitates teaching learning process. It has two phases, teacher and learner. In teacher phase, the teacher who is well-learned person transfers his/her knowledge to the learners to raise their grades/results; while in learner phase, learners/pupils learn and refine their knowledge through mutual interconnection. To solve constrained optimization problems (COPs) through TLBO we need to merge it with some constraint handling technique (CHT). Superiority of feasibility (SF) is a concept for making CHTs, existed in different forms based on various decisive factors. Most commonly used decision making factors in SF are number of constraints violated (NCV) and weighted mean (WM) values for comparing solutions. 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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="114112389"><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/114112389/Certain_Topological_Indices_of_Non_Commuting_Graphs_for_Finite_Non_Abelian_Groups"><img alt="Research paper thumbnail of Certain Topological Indices of Non-Commuting Graphs for Finite Non-Abelian Groups" class="work-thumbnail" src="https://attachments.academia-assets.com/110896479/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/114112389/Certain_Topological_Indices_of_Non_Commuting_Graphs_for_Finite_Non_Abelian_Groups">Certain Topological Indices of Non-Commuting Graphs for Finite Non-Abelian Groups</a></div><div class="wp-workCard_item"><span>Molecules</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A topological index is a number derived from a molecular structure (i.e., a graph) that represent...</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 topological index is a number derived from a molecular structure (i.e., a graph) that represents the fundamental structural characteristics of a suggested molecule. Various topological indices, including the atom-bond connectivity index, the geometric–arithmetic index, and the Randić index, can be utilized to determine various characteristics, such as physicochemical activity, chemical activity, and thermodynamic properties. Meanwhile, the non-commuting graph ΓG of a finite group G is a graph where non-central elements of G are its vertex set, while two different elements are edge connected when they do not commute in G. In this article, we investigate several topological properties of non-commuting graphs of finite groups, such as the Harary index, the harmonic index, the Randić index, reciprocal Wiener index, atomic-bond connectivity index, and the geometric–arithmetic index. In addition, we analyze the Hosoya characteristics, such as the Hosoya polynomial and the reciprocal sta...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08891266cec88a571c4def18e0cc9663" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":110896479,"asset_id":114112389,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/110896479/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="114112389"><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="114112389"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114112389; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114112389]").text(description); $(".js-view-count[data-work-id=114112389]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114112389; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114112389']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 114112389, 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: "08891266cec88a571c4def18e0cc9663" } } $('.js-work-strip[data-work-id=114112389]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114112389,"title":"Certain Topological Indices of Non-Commuting Graphs for Finite Non-Abelian Groups","translated_title":"","metadata":{"abstract":"A topological index is a number derived from a molecular structure (i.e., a graph) that represents the fundamental structural characteristics of a suggested molecule. Various topological indices, including the atom-bond connectivity index, the geometric–arithmetic index, and the Randić index, can be utilized to determine various characteristics, such as physicochemical activity, chemical activity, and thermodynamic properties. Meanwhile, the non-commuting graph ΓG of a finite group G is a graph where non-central elements of G are its vertex set, while two different elements are edge connected when they do not commute in G. In this article, we investigate several topological properties of non-commuting graphs of finite groups, such as the Harary index, the harmonic index, the Randić index, reciprocal Wiener index, atomic-bond connectivity index, and the geometric–arithmetic index. In addition, we analyze the Hosoya characteristics, such as the Hosoya polynomial and the reciprocal sta...","publisher":"MDPI AG","publication_name":"Molecules"},"translated_abstract":"A topological index is a number derived from a molecular structure (i.e., a graph) that represents the fundamental structural characteristics of a suggested molecule. Various topological indices, including the atom-bond connectivity index, the geometric–arithmetic index, and the Randić index, can be utilized to determine various characteristics, such as physicochemical activity, chemical activity, and thermodynamic properties. Meanwhile, the non-commuting graph ΓG of a finite group G is a graph where non-central elements of G are its vertex set, while two different elements are edge connected when they do not commute in G. In this article, we investigate several topological properties of non-commuting graphs of finite groups, such as the Harary index, the harmonic index, the Randić index, reciprocal Wiener index, atomic-bond connectivity index, and the geometric–arithmetic index. 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This article aims to investigate q-ROPFL correlation coefficient based on the proposed information energy and covariance formulas. Moreover, considering that different q-ROPFL elements may have varying criteria weights, the weighted correlation coefficient is further explored. Some desirable characteristics of the presented correlation coefficients are also discussed and proven. In addition, some theoretical development is provided, including the concept of composition matrix, correlation matrix, and equivalent correlation matrix via the proposed correlation coefficients. Then, a clustering algorithm is expanded where data is expressed in q-ROPFL form with unknown weight information and is explained through an illustrative example. 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Besides, detailed parameter analysis and comparative study are performed with the existing approaches to reveal the effectiveness of the f...","publisher":"Springer Science and Business Media LLC","publication_name":"Scientific Reports"},"translated_abstract":"q-ROPFLS, including numeric and linguistic data, has a wide range of applications in handling uncertain information. This article aims to investigate q-ROPFL correlation coefficient based on the proposed information energy and covariance formulas. Moreover, considering that different q-ROPFL elements may have varying criteria weights, the weighted correlation coefficient is further explored. Some desirable characteristics of the presented correlation coefficients are also discussed and proven. In addition, some theoretical development is provided, including the concept of composition matrix, correlation matrix, and equivalent correlation matrix via the proposed correlation coefficients. 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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="104821564"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/104821564/Hybrid_Honey_Bees_Meta_Heuristic_for_Benchmark_Data_Classification"><img alt="Research paper thumbnail of Hybrid Honey Bees Meta-Heuristic for Benchmark Data Classification" 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" rel="nofollow" href="https://www.academia.edu/104821564/Hybrid_Honey_Bees_Meta_Heuristic_for_Benchmark_Data_Classification">Hybrid Honey Bees Meta-Heuristic for Benchmark Data Classification</a></div><div class="wp-workCard_item"><span>Exploring Critical Approaches of Evolutionary Computation</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorith...</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">Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorithms. Sometimes, these algorithms cannot reach to global optima due to randomization and poor exploration and exploitation process. Here, the global artificial bee colony and Levenberq-Marquardt hybrid called GABC-LM algorithm is proposed. The proposed GABC-LM will use neural network for obtaining the accurate parameters, weights, and bias values for benchmark dataset classification. The performance of GABC-LM is benchmarked against NNs training with the typical LM, PSO, ABC, and GABC methods. The experimental result shows that the proposed GABC-LM performs better than that standard BP, ABC, PSO, and GABC for the classification task.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="104821564"><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="104821564"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821564; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104821564]").text(description); $(".js-view-count[data-work-id=104821564]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104821564; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104821564']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104821564, 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=104821564]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104821564,"title":"Hybrid Honey Bees Meta-Heuristic for Benchmark Data Classification","translated_title":"","metadata":{"abstract":"Some bio-inspired methods are cuckoo search, fish schooling, artificial bee colony (ABC) algorithms. 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The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. In this paper, an advanced amalgam of nature-inspired algorithms (ANIA) is developed by employing GWO and TLBO as constituent algorithms. 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The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. In this paper, an advanced amalgam of nature-inspired algorithms (ANIA) is developed by employing GWO and TLBO as constituent algorithms. Initially, an equal number of solutions are assigned to both NIAs to perform their search process of ...","publisher":"Hindawi Limited","publication_name":"Mathematical Problems in Engineering"},"translated_abstract":"Large-scale global optimization problems are ambitious and quite difficult to handle with deterministic methods. The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. In this paper, an advanced amalgam of nature-inspired algorithms (ANIA) is developed by employing GWO and TLBO as constituent algorithms. 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Continua</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9266ef27506d16b314eacb81526ffb44" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448164,"asset_id":104821560,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448164/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="104821560"><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="104821560"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821560; 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Grasshopper optimization algorithm (GOA) is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature, which has good convergence ability towards optima. The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS. The antecedent part parameters (Gaussian membership function parameters) are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm. Tuning of the consequent part parameters are accomplished using extreme learning machine. The optimized IT2-FLS (GOAIT2FELM) obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices. The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm. Analysis of the performance, on the same data-sets, reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.","publication_name":"Computers, Materials \u0026amp; Continua","grobid_abstract_attachment_id":104448164},"translated_abstract":null,"internal_url":"https://www.academia.edu/104821560/Optimization_of_Interval_Type_2_Fuzzy_Logic_System_Using_Grasshopper_Optimization_Algorithm","translated_internal_url":"","created_at":"2023-07-22T08:36:28.895-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":35135010,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":104448164,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448164/thumbnails/1.jpg","file_name":"TSP_CMC_22018.pdf","download_url":"https://www.academia.edu/attachments/104448164/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Optimization_of_Interval_Type_2_Fuzzy_Lo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448164/TSP_CMC_22018-libre.pdf?1690040363=\u0026response-content-disposition=attachment%3B+filename%3DOptimization_of_Interval_Type_2_Fuzzy_Lo.pdf\u0026Expires=1732704027\u0026Signature=VWp-X75AEbpRrfIPVX7d3Ly~kgvlfeZ9jtFkzB9PR3GYD2GQZuzoRwx6JuVwY~OlJEeUnyvi9h2uJ-OWz5kYJ9CH2TQx0woB-RkQCFgxzvf6tmLybPRDvZdeynrJNWuKE3V~RfkotXXVftr54GDlDwr8GBCq-RxHw-o8ATDyU2-m65bSu0LnxfQgiSscETkCHY5~EmM7QbPBCp3pRmjXLwDCTCZmTEp7-nwmqMwVDZU-ljJUMBoGmnlCJxoG-rvxPqLUcK~FyW0JyRw55QCSuPa-Yp05BaqYceRvX5zAgN1uSvAzsVSsxeRxv0mEjs~lGvV1iEjAuNCW1smfyFBMZg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Optimization_of_Interval_Type_2_Fuzzy_Logic_System_Using_Grasshopper_Optimization_Algorithm","translated_slug":"","page_count":19,"language":"en","content_type":"Work","owner":{"id":35135010,"first_name":"Prof.Dr.Wali Khan","middle_initials":null,"last_name":"Mashwani","page_name":"WaliMashwani","domain_name":"kust","created_at":"2015-09-21T11:30:13.814-07:00","display_name":"Prof.Dr.Wali Khan Mashwani","url":"https://kust.academia.edu/WaliMashwani"},"attachments":[{"id":104448164,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104448164/thumbnails/1.jpg","file_name":"TSP_CMC_22018.pdf","download_url":"https://www.academia.edu/attachments/104448164/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Optimization_of_Interval_Type_2_Fuzzy_Lo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104448164/TSP_CMC_22018-libre.pdf?1690040363=\u0026response-content-disposition=attachment%3B+filename%3DOptimization_of_Interval_Type_2_Fuzzy_Lo.pdf\u0026Expires=1732704027\u0026Signature=VWp-X75AEbpRrfIPVX7d3Ly~kgvlfeZ9jtFkzB9PR3GYD2GQZuzoRwx6JuVwY~OlJEeUnyvi9h2uJ-OWz5kYJ9CH2TQx0woB-RkQCFgxzvf6tmLybPRDvZdeynrJNWuKE3V~RfkotXXVftr54GDlDwr8GBCq-RxHw-o8ATDyU2-m65bSu0LnxfQgiSscETkCHY5~EmM7QbPBCp3pRmjXLwDCTCZmTEp7-nwmqMwVDZU-ljJUMBoGmnlCJxoG-rvxPqLUcK~FyW0JyRw55QCSuPa-Yp05BaqYceRvX5zAgN1uSvAzsVSsxeRxv0mEjs~lGvV1iEjAuNCW1smfyFBMZg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":56,"name":"Materials Engineering","url":"https://www.academia.edu/Documents/in/Materials_Engineering"},{"id":2698,"name":"Biomaterials","url":"https://www.academia.edu/Documents/in/Biomaterials"},{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":32984,"name":"Mechanics of Materials","url":"https://www.academia.edu/Documents/in/Mechanics_of_Materials"},{"id":64336,"name":"Population","url":"https://www.academia.edu/Documents/in/Population"},{"id":65501,"name":"Modelling and simulation","url":"https://www.academia.edu/Documents/in/Modelling_and_simulation"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":554780,"name":"Interdisciplinary Engineering","url":"https://www.academia.edu/Documents/in/Interdisciplinary_Engineering"},{"id":556845,"name":"Numerical Analysis and Computational Mathematics","url":"https://www.academia.edu/Documents/in/Numerical_Analysis_and_Computational_Mathematics"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"}],"urls":[{"id":33020011,"url":"https://www.techscience.com/cmc/v71n2/45804/pdf"}]}, 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="104821556"><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/104821556/Hybrid_Constrained_Evolutionary_Algorithm_For_Numerical_Optimization_Problems"><img alt="Research paper thumbnail of Hybrid Constrained Evolutionary Algorithm For Numerical Optimization Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/104448163/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/104821556/Hybrid_Constrained_Evolutionary_Algorithm_For_Numerical_Optimization_Problems">Hybrid Constrained Evolutionary Algorithm For Numerical Optimization Problems</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Constrained optimization are naturally arises in many real-life applications, and is therefore ga...</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">Constrained optimization are naturally arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers. Evolutionary algorithms are not directly applied on constrained optimization problems. However, different constraint-handling techniques are incorporated in their framework to adopt it for dealing with constrained environments. This paper suggests an hybrid constrained evolutionary algorithm (HCEA) that employs two penalty functions simultaneously. The suggested HCEA has two versions namely HCEA-static and HCEA-adaptive. The performance of the HCEA-static and HCEA-adaptive algorithms are examined upon the constrained benchmark functions that are recently designed for the special session of the 2006 IEEE Conference of Evolutionary Computation (IEEE-CEC&#39;06). The experimental results of the suggested algorithms are much promising as compared to one of the recent constrained version of the JADE. The converging behaviour of the both...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d44b042641566e887e8c0161fce836e9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104448163,"asset_id":104821556,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104448163/download_file?st=MTczMjcxNTA3NSw4LjIyMi4yMDguMTQ2&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="104821556"><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="104821556"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104821556; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104821556]").text(description); $(".js-view-count[data-work-id=104821556]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104821556; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104821556']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 104821556, 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: "d44b042641566e887e8c0161fce836e9" } } $('.js-work-strip[data-work-id=104821556]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104821556,"title":"Hybrid Constrained Evolutionary Algorithm For Numerical Optimization Problems","translated_title":"","metadata":{"abstract":"Constrained optimization are naturally arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers. 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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="104821554"><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/104821554/Applications_of_Mittag_Leffer_Type_Poisson_Distribution_to_a_Subclass_of_Analytic_Functions_Involving_Conic_Type_Regions"><img alt="Research paper thumbnail of Applications of Mittag-Leffer Type Poisson Distribution to a Subclass of Analytic Functions Involving Conic-Type Regions" class="work-thumbnail" src="https://attachments.academia-assets.com/104448137/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/104821554/Applications_of_Mittag_Leffer_Type_Poisson_Distribution_to_a_Subclass_of_Analytic_Functions_Involving_Conic_Type_Regions">Applications of Mittag-Leffer Type Poisson Distribution to a Subclass of Analytic Functions Involving Conic-Type Regions</a></div><div class="wp-workCard_item"><span>Journal of Function Spaces</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this article, we introduce a new subclass of analytic functions utilizing the idea of Mittag-L...</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">In this article, we introduce a new subclass of analytic functions utilizing the idea of Mittag-Leffler type Poisson distribution associated with the Janowski functions. 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