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Monkey Optimization Algorithm"]}" data-trace="false" data-dom-id="Pill-react-component-697f0584-97ce-4df1-ab3b-766a52bc2054"></div> <div id="Pill-react-component-697f0584-97ce-4df1-ab3b-766a52bc2054"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="10220761" href="https://www.academia.edu/Documents/in/Electronics_and_communication"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Electronics and communication"]}" data-trace="false" data-dom-id="Pill-react-component-e3d844a5-6155-4901-aa40-81db551ad958"></div> <div id="Pill-react-component-e3d844a5-6155-4901-aa40-81db551ad958"></div> </a></div></div><div class="external-links-container"><ul class="profile-links new-profile js-UserInfo-social"><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by rohit salgotra</h3></div><div class="js-work-strip profile--work_container" data-work-id="79440901"><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/79440901/Hybridizing_Cuckoo_Search_with_Naked_Mole_rat_Algorithm_Adapting_for_CEC_2017_and_CEC_2021_Test_Suites"><img alt="Research paper thumbnail of Hybridizing Cuckoo Search with Naked Mole-rat Algorithm: Adapting for CEC 2017 and CEC 2021 Test Suites" 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/79440901/Hybridizing_Cuckoo_Search_with_Naked_Mole_rat_Algorithm_Adapting_for_CEC_2017_and_CEC_2021_Test_Suites">Hybridizing Cuckoo Search with Naked Mole-rat Algorithm: Adapting for CEC 2017 and CEC 2021 Test Suites</a></div><div class="wp-workCard_item"><span>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Cuckoo search (CS) has proved its worth and is one among the most competitive algorithm for numer...</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">Cuckoo search (CS) has proved its worth and is one among the most competitive algorithm for numerical optimization. In order to improve its exploitation properties, this paper presents the hybridization of CS with a recently introduced naked mole-rat algorithm (NMRA). The major modification include $i$) new techniques based on barebones for global and NMRA for local search respectively are devised, ii) simulated annealing based mating factor for enhanced exploitation iii) an oscillating switch probability to balance between exploration and exploitation, and iv) shrinking population size reduction is used to minimize the computational burden. Apart from that, division of generations and population is also employed. The proposed mutation adaptive CS with MNRA (MaCN) is tested on CEC 2017 and CEC 2021 numerical benchmarks. From the experimental and statistical results, it can be said that MaCN is highly competitive with respect to MVMO, SaDN, JADE, SHADE, CV1.0, CSsin and CVnew algorithms.</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="79440901"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440901"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440901; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440901]").text(description); $(".js-view-count[data-work-id=79440901]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440901; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440901']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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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</script> <div class="js-work-strip profile--work_container" data-work-id="79440900"><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/79440900/A_hybridized_multi_algorithm_strategy_for_engineering_optimization_problems"><img alt="Research paper thumbnail of A hybridized multi-algorithm strategy for engineering optimization problems" 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/79440900/A_hybridized_multi_algorithm_strategy_for_engineering_optimization_problems">A hybridized multi-algorithm strategy for engineering optimization problems</a></div><div class="wp-workCard_item"><span>Knowledge-Based Systems</span><span>, 2021</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="79440900"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440900"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440900; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440900]").text(description); $(".js-view-count[data-work-id=79440900]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440900; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440900']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440900]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440900,"title":"A hybridized multi-algorithm strategy for engineering optimization problems","internal_url":"https://www.academia.edu/79440900/A_hybridized_multi_algorithm_strategy_for_engineering_optimization_problems","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="79440899"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/79440899/Comparison_of_range_based_versus_range_free_WSNs_localization_using_adaptive_SSA_algorithm"><img alt="Research paper thumbnail of Comparison of range-based versus range-free WSNs localization using adaptive SSA algorithm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/79440899/Comparison_of_range_based_versus_range_free_WSNs_localization_using_adaptive_SSA_algorithm">Comparison of range-based versus range-free WSNs localization using adaptive SSA algorithm</a></div><div class="wp-workCard_item"><span>Wireless Networks</span><span>, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79440899"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440899"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440899; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440899]").text(description); $(".js-view-count[data-work-id=79440899]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440899; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440899']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440899]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440899,"title":"Comparison of range-based versus range-free WSNs localization using adaptive SSA algorithm","internal_url":"https://www.academia.edu/79440899/Comparison_of_range_based_versus_range_free_WSNs_localization_using_adaptive_SSA_algorithm","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440898"><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/79440898/An_enhanced_grey_wolf_optimizer_for_numerical_optimization"><img alt="Research paper thumbnail of An enhanced grey wolf optimizer for numerical optimization" 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/79440898/An_enhanced_grey_wolf_optimizer_for_numerical_optimization">An enhanced grey wolf optimizer for numerical optimization</a></div><div class="wp-workCard_item"><span>2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent alg...</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">Grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent algorithms. The algorithm is based on the hunting pattern and leadership quality of grey wolfs present in nature. In this paper, to improve the working capabilities of GWO, a new version of GWO namely enhanced GWO (EGWO) has been proposed. The proposed version has been tested on standard benchmark problems to prove its competitiveness with respect to standard state-of-art algorithms. Experimental results show that EGWO is highly competitive and provide better convergence with respect to bat algorithm (BA), flower pollination algorithm (FPA), firefly algorithm (FA), bat flower pollinator (BFP) and GWO. Further convergence profiles validate the superior performance of EGWO.</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="79440898"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440898"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440898; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440898]").text(description); $(".js-view-count[data-work-id=79440898]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440898; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440898']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440896]").text(description); $(".js-view-count[data-work-id=79440896]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440896; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440896']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="79440895"><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/79440895/Effect_of_parametric_enhancements_on_naked_mole_rat_algorithm_for_global_optimization"><img alt="Research paper thumbnail of Effect of parametric enhancements on naked mole-rat algorithm for global optimization" 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/79440895/Effect_of_parametric_enhancements_on_naked_mole_rat_algorithm_for_global_optimization">Effect of parametric enhancements on naked mole-rat algorithm for global optimization</a></div><div class="wp-workCard_item"><span>Engineering With Computers</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating pattern...</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">Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating patterns of NMRs present in nature. The algorithm though is very simple and linear in nature but suffers from poor exploration during the initial stages and poor exploitation towards the end. Thus to overcome these problems and estimate the effect of basic parameters of NMRA, six new inertia weight strategies and five new mutation operators have been employed. After careful investigation, a new Levy mutated NMRA (LNMRA) is proposed. The new algorithm employs combined properties of inertia weights and mutation operators altogether. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes and testing is done on CEC 2005, CEC 2014 benchmark problems and real world optimization problem of dual band-notched ultra-wideband (UWB) antenna design. Experimental and statistical results show that the proposed LNMRA is better with respect to other ...</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="79440895"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440895"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440895; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440895]").text(description); $(".js-view-count[data-work-id=79440895]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440895; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440895']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440894]").text(description); $(".js-view-count[data-work-id=79440894]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440894; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440894']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440894]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440894,"title":"Artificial Intelligence for Fighting the COVID-19 Pandemic","internal_url":"https://www.academia.edu/79440894/Artificial_Intelligence_for_Fighting_the_COVID_19_Pandemic","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440893"><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/79440893/Linear_antenna_array_synthesis_using_bat_flower_pollinator"><img alt="Research paper thumbnail of Linear antenna array synthesis using bat flower pollinator" 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/79440893/Linear_antenna_array_synthesis_using_bat_flower_pollinator">Linear antenna array synthesis using bat flower pollinator</a></div><div class="wp-workCard_item"><span>2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Linear antenna arrays design is complex electromagnetic problem. In this work, LAA is designed us...</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">Linear antenna arrays design is complex electromagnetic problem. In this work, LAA is designed using bat flower pollination (BFP) to obtain required radiation pattern having minimum possible side lobe level (SLL). The bat flower pollination is a combination of two algorithms namely bat algorithm and flower pollination algorithm. The two algorithms have been combined so as to avoid the local minima and provide superior performance. The BFP algorithm has been used to optimize three different equally spaced LAA. The results have been compared with the popular algorithms like particle swarm optimization (PSO), biogeography based optimization (BBO), flower pollination algorithm (FPA) and others.</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="79440893"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440893"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440893; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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</script> <div class="js-work-strip profile--work_container" data-work-id="79440891"><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/79440891/Application_of_mutation_operators_to_salp_swarm_algorithm"><img alt="Research paper thumbnail of Application of mutation operators to salp swarm algorithm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/79440891/Application_of_mutation_operators_to_salp_swarm_algorithm">Application of mutation operators to salp swarm algorithm</a></div><div class="wp-workCard_item"><span>Expert Systems with Applications</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a very competitive algorithm and has proved its worth as an excellent problem optimizer. Though SSA is a very challenging algorithm but it suffers from the problem of poor exploitation, local optima stagnation and unbalanced exploration and exploitation operations. Thus in order to mitigate these problems and improve the working properties, seven new versions of SSA are proposed in present work. All the new versions employ new set of mutation properties along with some common properties. The common properties of all the algorithms include division of generations, adaptive switching and adaptive population strategy. Overall, the proposed algorithms are self-adaptive in nature along with some added mutation properties. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes. The best among the proposed is then tested on CEC 2005, CEC 2015 benchmark problems and real world problems from CEC 2011 benchmarks. Experimental and statistical results show that the proposed mutation clock SSA (MSSA) is best among all the algorithms under comparison.</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="79440891"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440891"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440891; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440891]").text(description); $(".js-view-count[data-work-id=79440891]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440891; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440891']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440891]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440891,"title":"Application of mutation operators to salp swarm algorithm","internal_url":"https://www.academia.edu/79440891/Application_of_mutation_operators_to_salp_swarm_algorithm","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440890"><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/79440890/An_improved_firefly_algorithm_for_numerical_optimization"><img alt="Research paper thumbnail of An improved firefly algorithm for numerical optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/86154906/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/79440890/An_improved_firefly_algorithm_for_numerical_optimization">An improved firefly algorithm for numerical optimization</a></div><div class="wp-workCard_item"><span>2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Firefly algorithm (FA) is a recently proposed meta-heuristic optimisation technique, which has sh...</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">Firefly algorithm (FA) is a recently proposed meta-heuristic optimisation technique, which has shown good performance on many optimisation problems. In the original FA, each firefly is attracted by any other brighter firefly (better fitness value). By the attraction, fireflies maybe moved to better positions. However, the attraction does not guarantee whether a firefly is moved to a better position. Sometimes, the attraction may move a firefly to a worse position. Therefore, the search of firefly is oscillated during the evolution. In this paper, we present an improved firefly algorithm (IFA), which employs a greedy selection method to guarantee that a firefly is not moved to worse positions. To verify the performance of IFA, a set of well-known benchmark functions are used in the experiments. Experimental results show that the IFA achieves better results than the original FA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="74b54579064474d6ae708c5ffdc5c549" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86154906,"asset_id":79440890,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86154906/download_file?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="79440890"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440890"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440890; 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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="79440889"><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/79440889/Time_Series_Analysis_and_Forecast_of_the_COVID_19_Pandemic_in_India_using_Genetic_Programming"><img alt="Research paper thumbnail of Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming" class="work-thumbnail" src="https://attachments.academia-assets.com/86154905/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/79440889/Time_Series_Analysis_and_Forecast_of_the_COVID_19_Pandemic_in_India_using_Genetic_Programming">Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming</a></div><div class="wp-workCard_item"><span>Chaos, Solitons &amp; Fractals</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on ...</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">Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c33dae5b9baa6d32eb2f0fc6233d73e5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86154905,"asset_id":79440889,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86154905/download_file?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="79440889"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440889"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440889; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440889]").text(description); $(".js-view-count[data-work-id=79440889]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440889; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440889']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="79440888"><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/79440888/Trust_aware_energy_efficient_stable_clustering_approach_using_fuzzy_type_2_Cuckoo_search_optimization_algorithm_for_wireless_sensor_networks"><img alt="Research paper thumbnail of Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks" 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/79440888/Trust_aware_energy_efficient_stable_clustering_approach_using_fuzzy_type_2_Cuckoo_search_optimization_algorithm_for_wireless_sensor_networks">Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks</a></div><div class="wp-workCard_item"><span>Wireless Networks</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With the advancement of communication and sensor technologies, it has become possible to develop ...</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">With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely wireless sensor networks (WSNs), can be used in a multitude of applications like healthcare, intelligent sectors, environmental sensing, and military defense. The crucial problem of WSN is the reliable exchange of data between different sensors and efficient communication with the data collection center. Clustering is the most appropriate approach to prolong the performance parameters of WSN. To overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime; an effective CH selection algorithm, optimized routing protocol, and trust management are required to design an effective WSN solution. In this paper, a Cuckoo search optimization algorithm using a fuzzy type-2 logic-based clustering strategy is suggested to extend the level of confidence and hence network lifespan. In intra-cluster communication, a threshold-based data transmission algorithm is used and a multi-hop routing scheme for inter-cluster communication is employed to decrease dissipated energy from CHs far away from BS. Simulation outcomes indicate that the proposed strategy outperforms other communication techniques in the context of the successful elimination of malicious nodes along with energy consumption, stability period, and network lifetime.</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="79440888"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440888"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440888; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440888]").text(description); $(".js-view-count[data-work-id=79440888]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440888; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440888']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440888]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440888,"title":"Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks","internal_url":"https://www.academia.edu/79440888/Trust_aware_energy_efficient_stable_clustering_approach_using_fuzzy_type_2_Cuckoo_search_optimization_algorithm_for_wireless_sensor_networks","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440887"><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/79440887/The_naked_mole_rat_algorithm"><img alt="Research paper thumbnail of The naked mole-rat algorithm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/79440887/The_naked_mole_rat_algorithm">The naked mole-rat algorithm</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) ...</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 work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) algorithm. This NMR algorithm mimics the mating patterns of NMRs present in nature. Two types of NMRs called workers and breeders are found to depict these patterns. Workers work continuously in the endeavor to become breeders, while breeders compete among themselves to mate with the queen. Those breeders who become sterile are pushed back to the worker’s group, and the fittest worker becomes a new breeder. This phenomenon has been adapted to develop the NMR algorithm. The algorithm has been benchmarked on 27 well-known test functions, and its performance is evaluated by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), differential evolution (DE), gravitational search algorithm (GSA), fast evolutionary programming (FEP), bat algorithm (BA), flower pollination algorithm (FPA), and firefly algorithm (FA). The experimental results and statistical analysis prove that NMR algorithm is very competitive as compared to other state-of-the-art algorithms. The matlab code for NMR algorithm is avaliable at <a href="https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm" rel="nofollow">https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm</a>.</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="79440887"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440887"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440887; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440887]").text(description); $(".js-view-count[data-work-id=79440887]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440887; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440887']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440887]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440887,"title":"The naked mole-rat algorithm","internal_url":"https://www.academia.edu/79440887/The_naked_mole_rat_algorithm","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440886"><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/79440886/On_the_improvement_in_grey_wolf_optimization"><img alt="Research paper thumbnail of On the improvement in grey wolf optimization" 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/79440886/On_the_improvement_in_grey_wolf_optimization">On the improvement in grey wolf optimization</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method w...</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">Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. Though the algorithm is very competitive and has been applied to various fields of research, it has poor exploration capability and suffers from local optima stagnation. So, in order to improve the explorative abilities of GWO, an extended version of grey wolf optimization (GWO-E) algorithm is presented. This newly proposed algorithm consists of two modifications: Firstly, it is able to explore new areas in the search space because of diverse positions assigned to the leaders. This helps in increasing the exploration and avoids local optima stagnation problem. Secondly, an opposition-based learning method has been used in the initial half of iterations to provide diversity among the search agents. The proposed approach has been tested on standard benchmarking functions for different population and dimension sizes to prove its effectiveness over other state-of-the-art algorithms. Experimental results show that the GWO-E algorithm performs better than GWO, bat algorithm, bat flower pollinator, chicken swarm optimization, differential evolution, firefly algorithm, flower pollination algorithm (FPA) and grasshopper optimization algorithm. Statistical testing of GWO-E has been done to prove its significance over other popular algorithms. Further, as a real-world application, the GWO-E is used to design non-uniform linear antenna array (LAA) for minimum possible sidelobe level and null control. Performance of GWO-E for the synthesis of LAA is evaluated by considering the several different case studies of LAA that exists in the literature, and the results are compared with the results of other popular meta-heuristic algorithms like genetic algorithm, ant lion algorithm, FPA, cat swarm optimization, GWO and many more. Numerical results further show the superior performance of GWO-E over original GWO and other popular algorithms.</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="79440886"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440886"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440886; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440886]").text(description); $(".js-view-count[data-work-id=79440886]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440886; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440886']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440886]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440886,"title":"On the improvement in grey wolf optimization","internal_url":"https://www.academia.edu/79440886/On_the_improvement_in_grey_wolf_optimization","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440885"><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/79440885/An_enhanced_moth_flame_optimization"><img alt="Research paper thumbnail of An enhanced moth flame optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/86154913/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/79440885/An_enhanced_moth_flame_optimization">An enhanced moth flame optimization</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Moth flame optimization (MFO) is a recent nature-inspired algorithm, motivated from the transvers...</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">Moth flame optimization (MFO) is a recent nature-inspired algorithm, motivated from the transverse orientation of moths in nature. The transverse orientation is a special kind of navigation method, which demonstrates the movement of moths toward moon in a straight path. This algorithm has been successfully applied on various optimization problems. But, MFO suffers from the problem of poor exploration. So, in order to enhance the performance of MFO, some modifications are proposed. A Cauchy distribution function is added to enhance the exploration capability, influence of best flame has been added to improve the exploitation and adaptive step size and division of iterations is followed to maintain a balance between the exploration and exploitation. The proposed algorithm has been named as enhanced moth flame optimization (E-MFO) and to validate the applicability of E-MFO, and it has been applied to twenty benchmark functions. Also, comprehensive comparison of E-MFO with other meta-heuristic algorithms like bat algorithm, bat flower pollination, differential evolution, firefly algorithm, genetic algorithm, particle swarm optimization and flower pollination algorithm has been done. Further, the effect of population and dimension size on the performance of MFO and E-MFO has been discussed. The experimental analysis shows the superior performance of E-MFO over other algorithms in terms of convergence rate and solution quality. Also, statistical testing of E-MFO has been done to prove its significance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0b22888ff358b3463e063cbff88b5d70" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86154913,"asset_id":79440885,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86154913/download_file?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="79440885"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440885"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440885; 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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="79440884"><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/79440884/An_energy_efficient_stable_clustering_approach_using_fuzzy_extended_grey_wolf_optimization_algorithm_for_WSNs"><img alt="Research paper thumbnail of An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs" 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/79440884/An_energy_efficient_stable_clustering_approach_using_fuzzy_extended_grey_wolf_optimization_algorithm_for_WSNs">An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs</a></div><div class="wp-workCard_item"><span>Wireless Networks</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Wireless sensor network (WSN) is a cost-effective networking solution for information updating in...</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">Wireless sensor network (WSN) is a cost-effective networking solution for information updating in the coverage radius or in the sensing region. To record a real-time event, a large number of sensor nodes (SNs) need to be arranged systematically, such that information collection is possible for a longer span of time. But, the hurdle faced by WSN is the limited resources of SNs. Hence, there is a high demand to design and implement an energy-efficient scheme to prolong the performance parameters of WSN. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance, and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering based hierarchical approach, efficient CH selection algorithm, and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy extended grey wolf optimization algorithm based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.</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="79440884"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440884"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440884; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440884]").text(description); $(".js-view-count[data-work-id=79440884]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440884; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440884']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440884]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440884,"title":"An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs","internal_url":"https://www.academia.edu/79440884/An_energy_efficient_stable_clustering_approach_using_fuzzy_extended_grey_wolf_optimization_algorithm_for_WSNs","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440883"><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/79440883/On_Some_Improved_Versions_of_Whale_Optimization_Algorithm"><img alt="Research paper thumbnail of On Some Improved Versions of Whale Optimization Algorithm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/79440883/On_Some_Improved_Versions_of_Whale_Optimization_Algorithm">On Some Improved Versions of Whale Optimization Algorithm</a></div><div class="wp-workCard_item"><span>Arabian Journal for Science and Engineering</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm whi...</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">Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm which is inspired from the social behavior of humpback whale. This algorithm mimics the bubble-net hunting strategy of whales and has been applied to optimization problems. But the algorithm suffers from the problem of poor exploration and local optima stagnation. In this paper, three different modified algorithms of WOA have been proposed to improve its explorative ability. The modified versions are based on the concepts of opposition-based learning, exponentially decreasing parameters and elimination or re-initialization of worst particles. These properties have been added to improve the explorative properties of WOA by maintaining diversity among the search agents. The proposed algorithms have been tested on CEC2005 benchmark problems for variable population and dimension sizes. Statistical testing and scalability testing of the best algorithm have been carried out to prove its significance over other algorithms such as with well-known algorithms such as bat algorithm, bat flower pollinator, differential evolution, firefly algorithm, flower pollination algorithm. It has been found from the experimental results that the performance of all the proposed versions is better than the original WOA. Here, opposition- and exponential-based WOA is the best among all the proposed variants. Statistical testing and convergence profiles further validate the results.</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="79440883"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440883"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440883; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440883]").text(description); $(".js-view-count[data-work-id=79440883]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440883; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440883']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440883]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440883,"title":"On Some Improved Versions of Whale Optimization Algorithm","internal_url":"https://www.academia.edu/79440883/On_Some_Improved_Versions_of_Whale_Optimization_Algorithm","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440882"><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/79440882/An_energy_efficient_stable_clustering_approach_using_fuzzy_enhanced_flower_pollination_algorithm_for_WSNs"><img alt="Research paper thumbnail of An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs" 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/79440882/An_energy_efficient_stable_clustering_approach_using_fuzzy_enhanced_flower_pollination_algorithm_for_WSNs">An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Due to advancement in the technology and need for machine-to-machine connectivity, wireless senso...</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">Due to advancement in the technology and need for machine-to-machine connectivity, wireless sensor network (WSN) overplays the role compared to other wireless networks. In this context, different applications based on WSNs need to be executed efficiently in terms of energy and communication. To achieve this, there is a need to collaborate among various devices at various levels. This can be achieved by the grouping of these devices, that is, through the clustering. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering-based hierarchical approach, efficient CH selection algorithm and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy-enhanced flower pollination algorithm-based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.</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="79440882"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440882"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440882; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440882]").text(description); $(".js-view-count[data-work-id=79440882]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440882; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440882']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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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</script> <div class="js-work-strip profile--work_container" data-work-id="79440881"><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/79440881/Tree_Based_Threshold_Sensitive_Energy_Efficient_Routing_Approach_For_Wireless_Sensor_Networks"><img alt="Research paper thumbnail of Tree-Based Threshold-Sensitive Energy-Efficient Routing Approach For Wireless Sensor Networks" 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/79440881/Tree_Based_Threshold_Sensitive_Energy_Efficient_Routing_Approach_For_Wireless_Sensor_Networks">Tree-Based Threshold-Sensitive Energy-Efficient Routing Approach For Wireless Sensor Networks</a></div><div class="wp-workCard_item"><span>Wireless Personal Communications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The widespread use of wireless sensor devices and their advancements in terms of size, deployment...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation method in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. In this paper, a tree based clustering approach named threshold-sensitive energy-efficient tree-based routing protocol is proposed using enhanced flower pollination algorithm to extend the operational lifetime of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in terms of energy consumption, stability period and system lifetime.</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="79440881"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440881"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440881; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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</script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="14951424" id="papers"><div class="js-work-strip profile--work_container" data-work-id="79440901"><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/79440901/Hybridizing_Cuckoo_Search_with_Naked_Mole_rat_Algorithm_Adapting_for_CEC_2017_and_CEC_2021_Test_Suites"><img alt="Research paper thumbnail of Hybridizing Cuckoo Search with Naked Mole-rat Algorithm: Adapting for CEC 2017 and CEC 2021 Test Suites" 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/79440901/Hybridizing_Cuckoo_Search_with_Naked_Mole_rat_Algorithm_Adapting_for_CEC_2017_and_CEC_2021_Test_Suites">Hybridizing Cuckoo Search with Naked Mole-rat Algorithm: Adapting for CEC 2017 and CEC 2021 Test Suites</a></div><div class="wp-workCard_item"><span>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Cuckoo search (CS) has proved its worth and is one among the most competitive algorithm for numer...</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">Cuckoo search (CS) has proved its worth and is one among the most competitive algorithm for numerical optimization. In order to improve its exploitation properties, this paper presents the hybridization of CS with a recently introduced naked mole-rat algorithm (NMRA). The major modification include $i$) new techniques based on barebones for global and NMRA for local search respectively are devised, ii) simulated annealing based mating factor for enhanced exploitation iii) an oscillating switch probability to balance between exploration and exploitation, and iv) shrinking population size reduction is used to minimize the computational burden. Apart from that, division of generations and population is also employed. The proposed mutation adaptive CS with MNRA (MaCN) is tested on CEC 2017 and CEC 2021 numerical benchmarks. From the experimental and statistical results, it can be said that MaCN is highly competitive with respect to MVMO, SaDN, JADE, SHADE, CV1.0, CSsin and CVnew algorithms.</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="79440901"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440901"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440901; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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</script> <div class="js-work-strip profile--work_container" data-work-id="79440900"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/79440900/A_hybridized_multi_algorithm_strategy_for_engineering_optimization_problems"><img alt="Research paper thumbnail of A hybridized multi-algorithm strategy for engineering optimization problems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/79440900/A_hybridized_multi_algorithm_strategy_for_engineering_optimization_problems">A hybridized multi-algorithm strategy for engineering optimization problems</a></div><div class="wp-workCard_item"><span>Knowledge-Based Systems</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79440900"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440900"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440900; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440899]").text(description); $(".js-view-count[data-work-id=79440899]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440899; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440899']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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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</script> <div class="js-work-strip profile--work_container" data-work-id="79440898"><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/79440898/An_enhanced_grey_wolf_optimizer_for_numerical_optimization"><img alt="Research paper thumbnail of An enhanced grey wolf optimizer for numerical optimization" 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/79440898/An_enhanced_grey_wolf_optimizer_for_numerical_optimization">An enhanced grey wolf optimizer for numerical optimization</a></div><div class="wp-workCard_item"><span>2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent alg...</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">Grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent algorithms. The algorithm is based on the hunting pattern and leadership quality of grey wolfs present in nature. In this paper, to improve the working capabilities of GWO, a new version of GWO namely enhanced GWO (EGWO) has been proposed. The proposed version has been tested on standard benchmark problems to prove its competitiveness with respect to standard state-of-art algorithms. Experimental results show that EGWO is highly competitive and provide better convergence with respect to bat algorithm (BA), flower pollination algorithm (FPA), firefly algorithm (FA), bat flower pollinator (BFP) and GWO. Further convergence profiles validate the superior performance of EGWO.</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="79440898"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440898"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440898; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440898]").text(description); $(".js-view-count[data-work-id=79440898]").attr('title', description).tooltip(); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440896]").text(description); $(".js-view-count[data-work-id=79440896]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440896; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440896']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="79440895"><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/79440895/Effect_of_parametric_enhancements_on_naked_mole_rat_algorithm_for_global_optimization"><img alt="Research paper thumbnail of Effect of parametric enhancements on naked mole-rat algorithm for global optimization" 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/79440895/Effect_of_parametric_enhancements_on_naked_mole_rat_algorithm_for_global_optimization">Effect of parametric enhancements on naked mole-rat algorithm for global optimization</a></div><div class="wp-workCard_item"><span>Engineering With Computers</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating pattern...</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">Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating patterns of NMRs present in nature. The algorithm though is very simple and linear in nature but suffers from poor exploration during the initial stages and poor exploitation towards the end. Thus to overcome these problems and estimate the effect of basic parameters of NMRA, six new inertia weight strategies and five new mutation operators have been employed. After careful investigation, a new Levy mutated NMRA (LNMRA) is proposed. The new algorithm employs combined properties of inertia weights and mutation operators altogether. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes and testing is done on CEC 2005, CEC 2014 benchmark problems and real world optimization problem of dual band-notched ultra-wideband (UWB) antenna design. Experimental and statistical results show that the proposed LNMRA is better with respect to other ...</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="79440895"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440895"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440895; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440895]").text(description); $(".js-view-count[data-work-id=79440895]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440895; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440895']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440894]").text(description); $(".js-view-count[data-work-id=79440894]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440894; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440894']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440894]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440894,"title":"Artificial Intelligence for Fighting the COVID-19 Pandemic","internal_url":"https://www.academia.edu/79440894/Artificial_Intelligence_for_Fighting_the_COVID_19_Pandemic","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440893"><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/79440893/Linear_antenna_array_synthesis_using_bat_flower_pollinator"><img alt="Research paper thumbnail of Linear antenna array synthesis using bat flower pollinator" 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/79440893/Linear_antenna_array_synthesis_using_bat_flower_pollinator">Linear antenna array synthesis using bat flower pollinator</a></div><div class="wp-workCard_item"><span>2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Linear antenna arrays design is complex electromagnetic problem. In this work, LAA is designed us...</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">Linear antenna arrays design is complex electromagnetic problem. In this work, LAA is designed using bat flower pollination (BFP) to obtain required radiation pattern having minimum possible side lobe level (SLL). The bat flower pollination is a combination of two algorithms namely bat algorithm and flower pollination algorithm. The two algorithms have been combined so as to avoid the local minima and provide superior performance. The BFP algorithm has been used to optimize three different equally spaced LAA. The results have been compared with the popular algorithms like particle swarm optimization (PSO), biogeography based optimization (BBO), flower pollination algorithm (FPA) and others.</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="79440893"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440893"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440893; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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</script> <div class="js-work-strip profile--work_container" data-work-id="79440891"><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/79440891/Application_of_mutation_operators_to_salp_swarm_algorithm"><img alt="Research paper thumbnail of Application of mutation operators to salp swarm algorithm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/79440891/Application_of_mutation_operators_to_salp_swarm_algorithm">Application of mutation operators to salp swarm algorithm</a></div><div class="wp-workCard_item"><span>Expert Systems with Applications</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a very competitive algorithm and has proved its worth as an excellent problem optimizer. Though SSA is a very challenging algorithm but it suffers from the problem of poor exploitation, local optima stagnation and unbalanced exploration and exploitation operations. Thus in order to mitigate these problems and improve the working properties, seven new versions of SSA are proposed in present work. All the new versions employ new set of mutation properties along with some common properties. The common properties of all the algorithms include division of generations, adaptive switching and adaptive population strategy. Overall, the proposed algorithms are self-adaptive in nature along with some added mutation properties. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes. The best among the proposed is then tested on CEC 2005, CEC 2015 benchmark problems and real world problems from CEC 2011 benchmarks. Experimental and statistical results show that the proposed mutation clock SSA (MSSA) is best among all the algorithms under comparison.</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="79440891"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440891"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440891; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440891]").text(description); $(".js-view-count[data-work-id=79440891]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440891; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440891']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440891]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440891,"title":"Application of mutation operators to salp swarm algorithm","internal_url":"https://www.academia.edu/79440891/Application_of_mutation_operators_to_salp_swarm_algorithm","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440890"><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/79440890/An_improved_firefly_algorithm_for_numerical_optimization"><img alt="Research paper thumbnail of An improved firefly algorithm for numerical optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/86154906/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/79440890/An_improved_firefly_algorithm_for_numerical_optimization">An improved firefly algorithm for numerical optimization</a></div><div class="wp-workCard_item"><span>2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Firefly algorithm (FA) is a recently proposed meta-heuristic optimisation technique, which has sh...</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">Firefly algorithm (FA) is a recently proposed meta-heuristic optimisation technique, which has shown good performance on many optimisation problems. In the original FA, each firefly is attracted by any other brighter firefly (better fitness value). By the attraction, fireflies maybe moved to better positions. However, the attraction does not guarantee whether a firefly is moved to a better position. Sometimes, the attraction may move a firefly to a worse position. Therefore, the search of firefly is oscillated during the evolution. In this paper, we present an improved firefly algorithm (IFA), which employs a greedy selection method to guarantee that a firefly is not moved to worse positions. To verify the performance of IFA, a set of well-known benchmark functions are used in the experiments. Experimental results show that the IFA achieves better results than the original FA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="74b54579064474d6ae708c5ffdc5c549" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86154906,"asset_id":79440890,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86154906/download_file?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="79440890"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440890"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440890; 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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="79440889"><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/79440889/Time_Series_Analysis_and_Forecast_of_the_COVID_19_Pandemic_in_India_using_Genetic_Programming"><img alt="Research paper thumbnail of Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming" class="work-thumbnail" src="https://attachments.academia-assets.com/86154905/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/79440889/Time_Series_Analysis_and_Forecast_of_the_COVID_19_Pandemic_in_India_using_Genetic_Programming">Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming</a></div><div class="wp-workCard_item"><span>Chaos, Solitons &amp; Fractals</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on ...</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">Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c33dae5b9baa6d32eb2f0fc6233d73e5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86154905,"asset_id":79440889,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86154905/download_file?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="79440889"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440889"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440889; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440889]").text(description); $(".js-view-count[data-work-id=79440889]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440889; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440889']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="79440888"><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/79440888/Trust_aware_energy_efficient_stable_clustering_approach_using_fuzzy_type_2_Cuckoo_search_optimization_algorithm_for_wireless_sensor_networks"><img alt="Research paper thumbnail of Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks" 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/79440888/Trust_aware_energy_efficient_stable_clustering_approach_using_fuzzy_type_2_Cuckoo_search_optimization_algorithm_for_wireless_sensor_networks">Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks</a></div><div class="wp-workCard_item"><span>Wireless Networks</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With the advancement of communication and sensor technologies, it has become possible to develop ...</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">With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely wireless sensor networks (WSNs), can be used in a multitude of applications like healthcare, intelligent sectors, environmental sensing, and military defense. The crucial problem of WSN is the reliable exchange of data between different sensors and efficient communication with the data collection center. Clustering is the most appropriate approach to prolong the performance parameters of WSN. To overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime; an effective CH selection algorithm, optimized routing protocol, and trust management are required to design an effective WSN solution. In this paper, a Cuckoo search optimization algorithm using a fuzzy type-2 logic-based clustering strategy is suggested to extend the level of confidence and hence network lifespan. In intra-cluster communication, a threshold-based data transmission algorithm is used and a multi-hop routing scheme for inter-cluster communication is employed to decrease dissipated energy from CHs far away from BS. Simulation outcomes indicate that the proposed strategy outperforms other communication techniques in the context of the successful elimination of malicious nodes along with energy consumption, stability period, and network lifetime.</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="79440888"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440888"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440888; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440888]").text(description); $(".js-view-count[data-work-id=79440888]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440888; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440888']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440888]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440888,"title":"Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks","internal_url":"https://www.academia.edu/79440888/Trust_aware_energy_efficient_stable_clustering_approach_using_fuzzy_type_2_Cuckoo_search_optimization_algorithm_for_wireless_sensor_networks","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440887"><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/79440887/The_naked_mole_rat_algorithm"><img alt="Research paper thumbnail of The naked mole-rat algorithm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/79440887/The_naked_mole_rat_algorithm">The naked mole-rat algorithm</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) ...</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 work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) algorithm. This NMR algorithm mimics the mating patterns of NMRs present in nature. Two types of NMRs called workers and breeders are found to depict these patterns. Workers work continuously in the endeavor to become breeders, while breeders compete among themselves to mate with the queen. Those breeders who become sterile are pushed back to the worker’s group, and the fittest worker becomes a new breeder. This phenomenon has been adapted to develop the NMR algorithm. The algorithm has been benchmarked on 27 well-known test functions, and its performance is evaluated by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), differential evolution (DE), gravitational search algorithm (GSA), fast evolutionary programming (FEP), bat algorithm (BA), flower pollination algorithm (FPA), and firefly algorithm (FA). The experimental results and statistical analysis prove that NMR algorithm is very competitive as compared to other state-of-the-art algorithms. The matlab code for NMR algorithm is avaliable at <a href="https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm" rel="nofollow">https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm</a>.</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="79440887"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440887"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440887; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440887]").text(description); $(".js-view-count[data-work-id=79440887]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440887; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440887']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440887]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440887,"title":"The naked mole-rat algorithm","internal_url":"https://www.academia.edu/79440887/The_naked_mole_rat_algorithm","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440886"><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/79440886/On_the_improvement_in_grey_wolf_optimization"><img alt="Research paper thumbnail of On the improvement in grey wolf optimization" 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/79440886/On_the_improvement_in_grey_wolf_optimization">On the improvement in grey wolf optimization</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method w...</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">Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. Though the algorithm is very competitive and has been applied to various fields of research, it has poor exploration capability and suffers from local optima stagnation. So, in order to improve the explorative abilities of GWO, an extended version of grey wolf optimization (GWO-E) algorithm is presented. This newly proposed algorithm consists of two modifications: Firstly, it is able to explore new areas in the search space because of diverse positions assigned to the leaders. This helps in increasing the exploration and avoids local optima stagnation problem. Secondly, an opposition-based learning method has been used in the initial half of iterations to provide diversity among the search agents. The proposed approach has been tested on standard benchmarking functions for different population and dimension sizes to prove its effectiveness over other state-of-the-art algorithms. Experimental results show that the GWO-E algorithm performs better than GWO, bat algorithm, bat flower pollinator, chicken swarm optimization, differential evolution, firefly algorithm, flower pollination algorithm (FPA) and grasshopper optimization algorithm. Statistical testing of GWO-E has been done to prove its significance over other popular algorithms. Further, as a real-world application, the GWO-E is used to design non-uniform linear antenna array (LAA) for minimum possible sidelobe level and null control. Performance of GWO-E for the synthesis of LAA is evaluated by considering the several different case studies of LAA that exists in the literature, and the results are compared with the results of other popular meta-heuristic algorithms like genetic algorithm, ant lion algorithm, FPA, cat swarm optimization, GWO and many more. Numerical results further show the superior performance of GWO-E over original GWO and other popular algorithms.</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="79440886"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440886"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440886; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440886]").text(description); $(".js-view-count[data-work-id=79440886]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440886; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440886']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440886]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440886,"title":"On the improvement in grey wolf optimization","internal_url":"https://www.academia.edu/79440886/On_the_improvement_in_grey_wolf_optimization","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440885"><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/79440885/An_enhanced_moth_flame_optimization"><img alt="Research paper thumbnail of An enhanced moth flame optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/86154913/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/79440885/An_enhanced_moth_flame_optimization">An enhanced moth flame optimization</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Moth flame optimization (MFO) is a recent nature-inspired algorithm, motivated from the transvers...</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">Moth flame optimization (MFO) is a recent nature-inspired algorithm, motivated from the transverse orientation of moths in nature. The transverse orientation is a special kind of navigation method, which demonstrates the movement of moths toward moon in a straight path. This algorithm has been successfully applied on various optimization problems. But, MFO suffers from the problem of poor exploration. So, in order to enhance the performance of MFO, some modifications are proposed. A Cauchy distribution function is added to enhance the exploration capability, influence of best flame has been added to improve the exploitation and adaptive step size and division of iterations is followed to maintain a balance between the exploration and exploitation. The proposed algorithm has been named as enhanced moth flame optimization (E-MFO) and to validate the applicability of E-MFO, and it has been applied to twenty benchmark functions. Also, comprehensive comparison of E-MFO with other meta-heuristic algorithms like bat algorithm, bat flower pollination, differential evolution, firefly algorithm, genetic algorithm, particle swarm optimization and flower pollination algorithm has been done. Further, the effect of population and dimension size on the performance of MFO and E-MFO has been discussed. The experimental analysis shows the superior performance of E-MFO over other algorithms in terms of convergence rate and solution quality. Also, statistical testing of E-MFO has been done to prove its significance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0b22888ff358b3463e063cbff88b5d70" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86154913,"asset_id":79440885,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86154913/download_file?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="79440885"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440885"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440885; 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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="79440884"><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/79440884/An_energy_efficient_stable_clustering_approach_using_fuzzy_extended_grey_wolf_optimization_algorithm_for_WSNs"><img alt="Research paper thumbnail of An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs" 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/79440884/An_energy_efficient_stable_clustering_approach_using_fuzzy_extended_grey_wolf_optimization_algorithm_for_WSNs">An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs</a></div><div class="wp-workCard_item"><span>Wireless Networks</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Wireless sensor network (WSN) is a cost-effective networking solution for information updating in...</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">Wireless sensor network (WSN) is a cost-effective networking solution for information updating in the coverage radius or in the sensing region. To record a real-time event, a large number of sensor nodes (SNs) need to be arranged systematically, such that information collection is possible for a longer span of time. But, the hurdle faced by WSN is the limited resources of SNs. Hence, there is a high demand to design and implement an energy-efficient scheme to prolong the performance parameters of WSN. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance, and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering based hierarchical approach, efficient CH selection algorithm, and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy extended grey wolf optimization algorithm based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.</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="79440884"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440884"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440884; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440884]").text(description); $(".js-view-count[data-work-id=79440884]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440884; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440884']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440884]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440884,"title":"An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs","internal_url":"https://www.academia.edu/79440884/An_energy_efficient_stable_clustering_approach_using_fuzzy_extended_grey_wolf_optimization_algorithm_for_WSNs","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440883"><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/79440883/On_Some_Improved_Versions_of_Whale_Optimization_Algorithm"><img alt="Research paper thumbnail of On Some Improved Versions of Whale Optimization Algorithm" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/79440883/On_Some_Improved_Versions_of_Whale_Optimization_Algorithm">On Some Improved Versions of Whale Optimization Algorithm</a></div><div class="wp-workCard_item"><span>Arabian Journal for Science and Engineering</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm whi...</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">Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm which is inspired from the social behavior of humpback whale. This algorithm mimics the bubble-net hunting strategy of whales and has been applied to optimization problems. But the algorithm suffers from the problem of poor exploration and local optima stagnation. In this paper, three different modified algorithms of WOA have been proposed to improve its explorative ability. The modified versions are based on the concepts of opposition-based learning, exponentially decreasing parameters and elimination or re-initialization of worst particles. These properties have been added to improve the explorative properties of WOA by maintaining diversity among the search agents. The proposed algorithms have been tested on CEC2005 benchmark problems for variable population and dimension sizes. Statistical testing and scalability testing of the best algorithm have been carried out to prove its significance over other algorithms such as with well-known algorithms such as bat algorithm, bat flower pollinator, differential evolution, firefly algorithm, flower pollination algorithm. It has been found from the experimental results that the performance of all the proposed versions is better than the original WOA. Here, opposition- and exponential-based WOA is the best among all the proposed variants. Statistical testing and convergence profiles further validate the results.</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="79440883"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440883"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440883; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440883]").text(description); $(".js-view-count[data-work-id=79440883]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440883; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440883']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=79440883]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79440883,"title":"On Some Improved Versions of Whale Optimization Algorithm","internal_url":"https://www.academia.edu/79440883/On_Some_Improved_Versions_of_Whale_Optimization_Algorithm","owner_id":10220761,"coauthors_can_edit":true,"owner":{"id":10220761,"first_name":"rohit","middle_initials":null,"last_name":"salgotra","page_name":"rohitsalgotra","domain_name":"chandigarhuniversity","created_at":"2014-03-18T04:16:46.227-07:00","display_name":"rohit salgotra","url":"https://chandigarhuniversity.academia.edu/rohitsalgotra"},"attachments":[]}, 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="79440882"><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/79440882/An_energy_efficient_stable_clustering_approach_using_fuzzy_enhanced_flower_pollination_algorithm_for_WSNs"><img alt="Research paper thumbnail of An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs" 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/79440882/An_energy_efficient_stable_clustering_approach_using_fuzzy_enhanced_flower_pollination_algorithm_for_WSNs">An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Due to advancement in the technology and need for machine-to-machine connectivity, wireless senso...</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">Due to advancement in the technology and need for machine-to-machine connectivity, wireless sensor network (WSN) overplays the role compared to other wireless networks. In this context, different applications based on WSNs need to be executed efficiently in terms of energy and communication. To achieve this, there is a need to collaborate among various devices at various levels. This can be achieved by the grouping of these devices, that is, through the clustering. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering-based hierarchical approach, efficient CH selection algorithm and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy-enhanced flower pollination algorithm-based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.</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="79440882"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440882"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440882; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79440882]").text(description); $(".js-view-count[data-work-id=79440882]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79440882; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79440882']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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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</script> <div class="js-work-strip profile--work_container" data-work-id="79440881"><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/79440881/Tree_Based_Threshold_Sensitive_Energy_Efficient_Routing_Approach_For_Wireless_Sensor_Networks"><img alt="Research paper thumbnail of Tree-Based Threshold-Sensitive Energy-Efficient Routing Approach For Wireless Sensor Networks" 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/79440881/Tree_Based_Threshold_Sensitive_Energy_Efficient_Routing_Approach_For_Wireless_Sensor_Networks">Tree-Based Threshold-Sensitive Energy-Efficient Routing Approach For Wireless Sensor Networks</a></div><div class="wp-workCard_item"><span>Wireless Personal Communications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The widespread use of wireless sensor devices and their advancements in terms of size, deployment...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation method in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. In this paper, a tree based clustering approach named threshold-sensitive energy-efficient tree-based routing protocol is proposed using enhanced flower pollination algorithm to extend the operational lifetime of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in terms of energy consumption, stability period and system lifetime.</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="79440881"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79440881"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79440881; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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