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window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":72108107,"created_at":"2022-02-21T07:44:39.486-08:00","from_world_paper_id":196514964,"updated_at":"2024-11-23T16:43:37.447-08:00","_data":{"publisher":"The Science and Information Organization","grobid_abstract":"Multi-objective EAs (MOEAs) are well established population-based techniques for solving various search and optimization problems. MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different problems. The use of multiple operators with a selfadaptive capability can further improve the performance of existing MOEAs. This paper suggests an enhanced version of a genetically adaptive multi-algorithm for multi-objective (AMAL-GAM) optimisation which includes differential evolution (DE), particle swarm optimization (PSO), simulated binary crossover (SBX), Pareto archive evolution strategy (PAES) and simplex crossover (SPX) for population evolution during the course of optimization. We examine the performance of this enhanced version of AMALGAM experimentally over two different test suites, the ZDT test problems and the test instances designed recently for the special session on MOEA's competition at the Congress of Evolutionary Computing of 2009 (CEC'09). The suggested algorithm has found better approximate solutions on most test problems in terms of inverted generational distance (IGD) as the metric indicator.","publication_date":"2015,,","publication_name":"International Journal of Advanced Computer Science and Applications","grobid_abstract_attachment_id":"81170758"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [27245290]; 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="" 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data-landing_url="https://www.academia.edu/72108107/Enhanced_Version_of_Multi_algorithm_Genetically_Adaptive_for_Multiobjective_optimization" 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="74133370" 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/74133370/A_Review_towards_Evolutionary_Multiobjective_optimization_Algorithms">A Review towards Evolutionary Multiobjective optimization 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="148870971" href="https://independent.academia.edu/sunnysharma186">sunny sharma</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2014</p><p class="ds-related-work--abstract ds2-5-body-sm">Multi objective optimization is a promising field which is increasingly being encountered in many areas worldwide. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used to solve Multi objective problems. Various multiobjective evolutionary algorithms have been developed. Their principal reason for development is their ability to find multiple Pareto optimal solution in single run. Their Basic motive of evolutionary multiobjective optimization in contrast to singleobjective optimization was optimality, decision making algorithm design (fitness, diversity, and elitism), constraints, and preference. The goal of this paper is to trace the genealogy & review the state of the art of evolutionary multiobjective optimization 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":"A Review towards Evolutionary Multiobjective optimization Algorithms","attachmentId":82394377,"attachmentType":"pdf","work_url":"https://www.academia.edu/74133370/A_Review_towards_Evolutionary_Multiobjective_optimization_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/74133370/A_Review_towards_Evolutionary_Multiobjective_optimization_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="1" data-entity-id="65703391" 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/65703391/GeDEA_II_A_Novel_Evolutionary_Algorithm_for_Multi_Objective_Optimization_Problems_Based_on_the_Simplex_Crossover_and_The_Shrink_Mutation">GeDEA-II : A Novel Evolutionary Algorithm for Multi-Objective Optimization Problems Based on the Simplex Crossover and The Shrink Mutation</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="202736334" href="https://unipd.academia.edu/ErnestoBenini">Ernesto Benini</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2012</p><p class="ds-related-work--abstract ds2-5-body-sm">The key issue for an efficient and reliable multiobjective evolutionary algorithm is the ability to converge to the True Pareto Front with the least number of objective function evaluations, while covering it as much as possible. To this purpose, in a previous paper performance comparisons showed that the Genetic Diversity Evolutionary Algorithm (GeDEA) was at the same level of the best state-of-the-art MOEAs due to it intrinsic ability to properly conjugate exploitation of current non-dominated solutions and the exploration of the search space. In this paper, an improved version, namely the GeDEAII, is proposed which features a novel crossover operator, the Simplex-Crossover, and a novel mutation operator, the ShrinkMutation. GeDEM operator was left unchanged and completed using the non-dominated-sorting based on crowding distance. 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