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This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this" /> <meta name="twitter:image" content="https://0.academia-photos.com/344652/1098577/1370066/s200_xin-she.yang.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/7395149/Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_" /> <meta property="og:title" content="Multi-objective Bat Algorithm (MOBA) demo (Matlab code)" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-teaching.gif" /> <meta property="og:description" content="The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this" /> <meta property="article:author" content="https://cambridge.academia.edu/XinSheYang" /> <meta name="description" content="The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this" /> <title>Multi-objective Bat Algorithm (MOBA) demo (Matlab code) | Xin-She Yang - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/7395149/Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = 'c7c923e13c2d00b99cbe85c9159af2026a636b1c'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1732704790000); window.Aedu.timeDifference = new Date().getTime() - 1732704790000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub) as well as certain parameters. Yang, Xin She. “Bat Algorithm for Multi-Objective Optimisation.” International Journal of Bio-Inspired Computation, vol. 3, no. 5, Inderscience Publishers, 2011, p. 267, doi:10.1504/ijbic.2011.042259. [Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]","author":[{"@context":"https://schema.org","@type":"Person","name":"Xin-She Yang"}],"contributor":[],"dateCreated":"2014-06-18","dateModified":null,"datePublished":null,"headline":"Multi-objective Bat Algorithm (MOBA) demo (Matlab code)","inLanguage":"en","keywords":["Multiculturalism","Optimization techniques","Bat Algorithm","Bio and Nature Inspired Algorithms"],"locationCreated":null,"publication":null,"publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/7395149/Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"cambridge"}]}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/single_work_page/loswp-102fa537001ba4d8dcd921ad9bd56c474abc201906ea4843e7e7efe9dfbf561d.css" /><link rel="stylesheet" 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This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub) as well as certain parameters.\r\n\r\nYang, Xin She. “Bat Algorithm for Multi-Objective Optimisation.” International Journal of Bio-Inspired Computation, vol. 3, no. 5, Inderscience Publishers, 2011, p. 267, doi:10.1504/ijbic.2011.042259.\r\n\r\n[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]","more_info":"Xin-She Yang","event_date":"2011,,"},"document_type":"teaching_document","pre_hit_view_count_baseline":null,"quality":"low","language":"en","title":"Multi-objective Bat Algorithm (MOBA) demo (Matlab code)","broadcastable":null,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [344652]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":62552813,"attachmentType":"txt"}"><img alt="First page of “Multi-objective Bat Algorithm (MOBA) demo (Matlab code)”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/62552813/mini_magick20200401-18331-1n8n2oi.png?1585772169" /><img alt="Academia Logo" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/academia-logo-redesign-2015-A-971495bd31377b9367f5ce5ca92bbc27dd8a843db989cbce3408389c670b62c0.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free TXT</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Multi-objective Bat Algorithm (MOBA) demo (Matlab code)</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="344652" href="https://cambridge.academia.edu/XinSheYang"><img alt="Profile image of Xin-She Yang" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/344652/1098577/1370066/s65_xin-she.yang.jpg" />Xin-She Yang</a></div><div class="ds-work-card--detail"></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub) as well as certain parameters. Yang, Xin She. “Bat Algorithm for Multi-Objective Optimisation.” International Journal of Bio-Inspired Computation, vol. 3, no. 5, Inderscience Publishers, 2011, p. 267, doi:10.1504/ijbic.2011.042259. [Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":62552813,"attachmentType":"txt","workUrl":"https://www.academia.edu/7395149/Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":62552813,"attachmentType":"txt","workUrl":"https://www.academia.edu/7395149/Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="62552813" data-landing_url="https://www.academia.edu/7395149/Multi_objective_Bat_Algorithm_MOBA_demo_Matlab_code_" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="94269469" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/94269469/Many_objective_BAT_algorithm">Many-objective BAT algorithm</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="251886344" href="https://independent.academia.edu/IrfanYounas12">Irfan Younas</a></div><p class="ds-related-work--metadata ds2-5-body-xs">PLOS ONE, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Many-objective BAT algorithm","attachmentId":96773391,"attachmentType":"pdf","work_url":"https://www.academia.edu/94269469/Many_objective_BAT_algorithm","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/94269469/Many_objective_BAT_algorithm"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="1504194" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/1504194/Bat_Algorithm_for_Multi_objective_Optimisation">Bat Algorithm for Multi-objective Optimisation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="344652" href="https://cambridge.academia.edu/XinSheYang">Xin-She Yang</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Engineering optimization is typically multiobjective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimization algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving nonlinear, global optimisation problems. In this paper, we extend this algorithm to solve multiobjective optimisation problems. The proposed multiobjective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multiobjective design problems such as welded beam design. 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BAT Algorithm with Many Objectives A several BAT algorithms based on R2 Distance (MaBAT/R2) is described, which blends the predominance notion with the R2 marker technique. While the R2 Indicator simplifies the multi-objective problem (MOP) by rewriting it as a series of Tchebycheff Approach problems, since this leader decision making uses the Tchebycheff Approach as a criterion, tackling these issues at the same time inside the BAT framework may lead to early converging. Predominance is important in constructing the leader's collection because it allows the chosen leaders to encompass fewer dense regions, avoiding local optima and producing a more diverse approximated Pareto front. 9 non-linear standard functions yielded this result. MaBAT/R2 appears to be more efficient than MOEAD, NSGAII, MPSOD, and SPEA2. 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