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Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way" /> <meta property="article:author" content="https://almas-rayaneh.academia.edu/aliaghaei" /> <meta name="description" content="In recent years, the application of metaheuristic techniques to solve multi-objective optimization problems (MOPs) has become an active research area. Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way" /> <title>(PDF) Multi-objective optimization using metaheuristics: non-standard algorithms | ali aghaei - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/39245047/Multi_objective_optimization_using_metaheuristics_non_standard_algorithms" /> <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 = '9387f500ddcbb8d05c67bef28a2fe0334f1aafb8'; 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(1732992972000); window.Aedu.timeDifference = new Date().getTime() - 1732992972000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"In recent years, the application of metaheuristic techniques to solve multi-objective optimization problems (MOPs) has become an active research area. Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfills the requirements of convergence to the true Pareto front and uniform diversity. Most studies on metaheuristics for multi-objective optimization are focused on Evolutionary Algorithms, and some of the state-of-the-art techniques belong to this class of algorithms. Our goal in this paper is to study open research lines related to metaheuristics but focusing on less explored areas to provide new perspectives to those researchers interested in multi-objective optimization. In particular, we focus on non-evolutionary metaheuristics, hybrid multi-objective metaheuristics, parallel multi-objective optimization, and multi-objective optimization under uncertainty. We analyze these issues and discuss open research lines.","author":[{"@context":"https://schema.org","@type":"Person","name":"ali aghaei"}],"contributor":[],"dateCreated":"2019-05-24","dateModified":"2019-05-24","datePublished":null,"headline":"Multi-objective optimization using metaheuristics: non-standard algorithms","inLanguage":"en","keywords":[],"locationCreated":null,"publication":null,"publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/39245047/Multi_objective_optimization_using_metaheuristics_non_standard_algorithms","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"almas-rayaneh"}]}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/single_work_page/loswp-102fa537001ba4d8dcd921ad9bd56c474abc201906ea4843e7e7efe9dfbf561d.css" /><link rel="stylesheet" media="all" 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Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfills the requirements of convergence to the true Pareto front and uniform diversity. Most studies on metaheuristics for multi-objective optimization are focused on Evolutionary Algorithms, and some of the state-of-the-art techniques belong to this class of algorithms. Our goal in this paper is to study open research lines related to metaheuristics but focusing on less explored areas to provide new perspectives to those researchers interested in multi-objective optimization. In particular, we focus on non-evolutionary metaheuristics, hybrid multi-objective metaheuristics, parallel multi-objective optimization, and multi-objective optimization under uncertainty. We analyze these issues and discuss open research lines."},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Multi-objective optimization using metaheuristics: non-standard algorithms","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [114599529]; 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":59377036,"attachmentType":"pdf"}"><img alt="First page of “Multi-objective optimization using metaheuristics: non-standard algorithms”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/59377036/mini_magick20190524-3609-1i6wyfn.png?1558683587" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.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 PDF</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 optimization using metaheuristics: non-standard algorithms</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="114599529" href="https://almas-rayaneh.academia.edu/aliaghaei"><img alt="Profile image of ali aghaei" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />ali aghaei</a></div><div class="ds-work-card--detail"></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">In recent years, the application of metaheuristic techniques to solve multi-objective optimization problems (MOPs) has become an active research area. Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfills the requirements of convergence to the true Pareto front and uniform diversity. Most studies on metaheuristics for multi-objective optimization are focused on Evolutionary Algorithms, and some of the state-of-the-art techniques belong to this class of algorithms. Our goal in this paper is to study open research lines related to metaheuristics but focusing on less explored areas to provide new perspectives to those researchers interested in multi-objective optimization. In particular, we focus on non-evolutionary metaheuristics, hybrid multi-objective metaheuristics, parallel multi-objective optimization, and multi-objective optimization under uncertainty. We analyze these issues and discuss open research lines.</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":59377036,"attachmentType":"pdf","workUrl":"https://www.academia.edu/39245047/Multi_objective_optimization_using_metaheuristics_non_standard_algorithms"}">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":59377036,"attachmentType":"pdf","workUrl":"https://www.academia.edu/39245047/Multi_objective_optimization_using_metaheuristics_non_standard_algorithms"}"><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="59377036" data-landing_url="https://www.academia.edu/39245047/Multi_objective_optimization_using_metaheuristics_non_standard_algorithms" 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="10000996" 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/10000996/Combining_Convergence_and_Diversity_in_Evolutionary_Multi_Objective_Optimization">Combining Convergence and Diversity in Evolutionary Multi-Objective Optimization</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="24337947" href="https://facebouk.academia.edu/lianhangdou">lianhang dou</a></div><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":"Combining Convergence and Diversity in Evolutionary Multi-Objective Optimization","attachmentId":36139491,"attachmentType":"pdf","work_url":"https://www.academia.edu/10000996/Combining_Convergence_and_Diversity_in_Evolutionary_Multi_Objective_Optimization","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/10000996/Combining_Convergence_and_Diversity_in_Evolutionary_Multi_Objective_Optimization"><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="99671680" 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/99671680/A_Meta_Objective_Approach_for_Many_Objective_Evolutionary_Optimization">A Meta-Objective Approach for Many-Objective Evolutionary Optimization</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="32956335" href="https://independent.academia.edu/GaryYen1">Gary Yen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Evolutionary Computation, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">Pareto-based multi-objective evolutionary algorithms experience grand challenges in solving many-objective optimization problems due to their inability to maintain both convergence and diversity in a high-dimensional objective space. 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Since MeO only converts the problem formulation, it can be readily incorporated within any multi-objective evolutionary algorithms, includin...</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":"A Meta-Objective Approach for Many-Objective Evolutionary Optimization","attachmentId":100699010,"attachmentType":"pdf","work_url":"https://www.academia.edu/99671680/A_Meta_Objective_Approach_for_Many_Objective_Evolutionary_Optimization","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/99671680/A_Meta_Objective_Approach_for_Many_Objective_Evolutionary_Optimization"><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="2" data-entity-id="26498886" 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/26498886/Convergence_speed_in_multi_objective_metaheuristics_Efficiency_criteria_and_empirical_study">Convergence speed in multi-objective metaheuristics: Efficiency criteria and empirical study</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="20642687" href="https://ucm.academia.edu/FranciscoParraLuna">Francisco Parra Luna</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal for Numerical Methods in Engineering, 2010</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":"Convergence speed in multi-objective metaheuristics: Efficiency criteria and empirical study","attachmentId":46795854,"attachmentType":"pdf","work_url":"https://www.academia.edu/26498886/Convergence_speed_in_multi_objective_metaheuristics_Efficiency_criteria_and_empirical_study","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/26498886/Convergence_speed_in_multi_objective_metaheuristics_Efficiency_criteria_and_empirical_study"><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="3" data-entity-id="74795810" 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/74795810/The_measure_of_Pareto_optima_applications_to_multi_objective_metaheuristics">The measure of Pareto optima applications to multi-objective metaheuristics</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="15568110" href="https://independent.academia.edu/MarkFleischer">Mark Fleischer</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Evolutionary Multi-Criterion Optimization, 2003</p><p class="ds-related-work--abstract ds2-5-body-sm">This article describes a set function that maps a set of Pareto optimal points to a scalar. 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Various multi-objective optimization algorithms have been developed to apply this method to problems. In multi-objective optimization algorithms, the pareto optimal method is used to find the appropriate solution set over the problems. In the Pareto optimal method, the Pareto optimal set, which consists of the solutions reached by the multi-objective optimization, includes all the best solutions of the problems in certain intervals. For this reason, the Pareto optimal method is a very effective method to find the closest value to the optimum. In this study, the Multi-Objective Golden Sine Algorithm we developed (MOGoldSA), the recently published Multi-Objective Artificial Hummingbird Algorithm (MOAHA), and the Non-Dominant Sequencing Genetic Algorithm II (NSGA-II), which has an important place among the multi-objective optimization algorithms in the literature, are ...</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":"Performance Analysis of Current Multi-Objective Metaheuristic Optimization Algorithms for Unconstrained Problems","attachmentId":95019099,"attachmentType":"pdf","work_url":"https://www.academia.edu/91848767/Performance_Analysis_of_Current_Multi_Objective_Metaheuristic_Optimization_Algorithms_for_Unconstrained_Problems","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/91848767/Performance_Analysis_of_Current_Multi_Objective_Metaheuristic_Optimization_Algorithms_for_Unconstrained_Problems"><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="6" data-entity-id="69566933" 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/69566933/Multi_Objective_Optimization_In_Theory_and_Practice_II_Metaheuristic_Algorithms">Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms</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="107259741" href="https://univ-paris1.academia.edu/Andr%C3%A9AKeller">André A. Keller</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2019</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":"Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms","attachmentId":79613902,"attachmentType":"pdf","work_url":"https://www.academia.edu/69566933/Multi_Objective_Optimization_In_Theory_and_Practice_II_Metaheuristic_Algorithms","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/69566933/Multi_Objective_Optimization_In_Theory_and_Practice_II_Metaheuristic_Algorithms"><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="7" data-entity-id="33778174" 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/33778174/Bi_goal_evolution_for_many_objective_optimization_problems">Bi-goal evolution for many-objective optimization problems</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="66143978" href="https://independent.academia.edu/PrativaAgarwalla">Prativa Agarwalla</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with multi-objective optimization problems with many objectives. In multi-objective optimization, it is generally observed that 1) the conflict between the proximity and diversity requirements is aggravated with the increase of the number of objectives and 2) the Pareto dominance loses its effectiveness for a high-dimensional space but works well on a low-dimensional space. Inspired by these two observations, BiGE converts a given multi-objective optimization problem into a bi-goal (objective) optimization problem regarding proximity and diversity, and then handles it using the Pareto dominance relation in this bi-goal domain. Implemented with estimation methods of individuals' performance and the classic Pareto nondominated sorting procedure, BiGE divides individuals into different nondominated layers and attempts to put well-converged and well-distributed individuals into the first few layers. From a series of extensive experiments on four groups of well-defined continuous and combinatorial optimization problems with 5, 10 and 15 objectives, BiGE has been found to be very competitive against five state-of-the-art algorithms in balancing proximity and diversity. The proposed approach is the first step towards a new way of addressing many-objective problems as well as indicating several important issues for future development of this type of algorithms.</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":"Bi-goal evolution for many-objective optimization problems","attachmentId":53766814,"attachmentType":"pdf","work_url":"https://www.academia.edu/33778174/Bi_goal_evolution_for_many_objective_optimization_problems","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/33778174/Bi_goal_evolution_for_many_objective_optimization_problems"><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="8" data-entity-id="115122171" 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/115122171/Multi_Objective_Evolutionary_Algorithms_Foundation_Development_and_Open_Issues">Multi-Objective Evolutionary Algorithms: Foundation, Development and Open Issues</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="257188964" href="https://independent.academia.edu/B%C3%B9iL%C3%A2m17">Bùi Lâm</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Computer Science and Cybernetics</p><p class="ds-related-work--abstract ds2-5-body-sm">Evolutionary computation (EC) has been a fascinating branch of computation inspiredby a natural phenomenal of evolution. EC enables computer scientists to design eective algorithmsdealing dicult problems. This paper focuses on a special class problem called multi-objective optimizationproblems and evolutionary algorithms designed for it. We will overview the development ofmulti-objective evolutionary algorithms (MOEAs) over the years and problem diculties and thenindicate the open problems in this area. Our chief goal is to provide readers reference material in thearea of multi-objective evolutionary algorithms</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":"Multi-Objective Evolutionary Algorithms: Foundation, Development and Open Issues","attachmentId":111623085,"attachmentType":"pdf","work_url":"https://www.academia.edu/115122171/Multi_Objective_Evolutionary_Algorithms_Foundation_Development_and_Open_Issues","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/115122171/Multi_Objective_Evolutionary_Algorithms_Foundation_Development_and_Open_Issues"><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="9" data-entity-id="94148526" 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/94148526/Survey_and_unification_of_local_search_techniques_in_metaheuristics_for_multi_objective_combinatorial_optimisation">Survey and unification of local search techniques in metaheuristics for multi-objective combinatorial 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="9335826" href="https://independent.academia.edu/LJourdan">Laetitia Jourdan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Heuristics, 2018</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":"Survey and unification of local search techniques in metaheuristics for multi-objective combinatorial optimisation","attachmentId":96686822,"attachmentType":"pdf","work_url":"https://www.academia.edu/94148526/Survey_and_unification_of_local_search_techniques_in_metaheuristics_for_multi_objective_combinatorial_optimisation","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/94148526/Survey_and_unification_of_local_search_techniques_in_metaheuristics_for_multi_objective_combinatorial_optimisation"><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></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":59377036,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":59377036,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_59377036" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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