{"id":1667,"date":"2024-03-04T21:46:56","date_gmt":"2024-03-04T21:46:56","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1667"},"modified":"2024-03-08T15:33:59","modified_gmt":"2024-03-08T15:33:59","slug":"vol21-no2-art5","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art5\/","title":{"rendered":"Dynamic Multi-Objective Evolutionary Algorithms: An Overview"},"content":{"rendered":"<p>Elaine Guerrero-Pe\u00f1a <a href=\"https:\/\/orcid.org\/0000-0003-3711-3490\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>Aluizio Fausto Ribeiro Ara\u00fajo <a href=\"https:\/\/orcid.org\/0000-0002-1749-2174\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, &amp; C\u00edcero Garrozi <a href=\"https:\/\/orcid.org\/0000-0002-8838-2386\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> Evolutionary algorithms have been widely explored and applied in optimization problems. The introduction of multi-objective evolutionary algorithms (MOEAs) has facilitated the adaptation and creation of new methods to handle more complex and realistic optimizations, such as dynamic multi-objective optimization problems (DMOPs). A dynamic MOEA (DMOEA) can be constructed by changing the MOEA structure and variation operators used to solve DMOPs. Furthermore, DMOEAs can implement change-detection strategies and mechanisms to handle the dynamics of the environment. DMOEAs are often designed to solve unconstrained DMOPs. However, several studies have been conducted to solve dynamic constrained MOPs and more recently, to solve dynamic many-objective optimization problems. There are many real-world DMOPs; however, few DMOEAs have been evaluated with respect to those problems. Evaluation of algorithm performance is an essential aspect of using DMOEAs. Several measures used to validate MOEAs have been adapted to assess DMOEAs and quantitatively compare them. This paper presents a broad review of DMOEAs. The information is summarized and organized according to current research branches. Furthermore, a more general classification scheme for DMOEAs is proposed, based on the method of incorporating diversity to solve DMOPs.<\/p>\n<p><strong>Keywords:<\/strong> Dynamic Multi-objective Optimization Problems, Dynamic Multi-objective Evolutionary Algorithms.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol21-no2-art5\">10.21528\/lnlm-vol21-no2-art5<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2023\/12\/vol21-no2-art5.pdf\">vol21-no2-art5.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2023\/12\/vol21-no2-art5.bib\">vol21-no2-art5.bib <\/a><\/p>\n<p><strong>Supplementary file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2023\/12\/vol21-no2-art5_sup.pdf\">vol21-no2-art5_sup.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Elaine Guerrero-Pe\u00f1a Aluizio Fausto Ribeiro Ara\u00fajo , &amp; C\u00edcero Garrozi Abstract: Evolutionary algorithms have been widely explored and applied in optimization problems. The introduction of multi-objective evolutionary algorithms (MOEAs) has facilitated the adaptation and creation of new methods to handle <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art5\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1613,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1667","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Dynamic Multi-Objective Evolutionary Algorithms: An Overview - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol21-no2\/vol21-no2-art5\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Dynamic Multi-Objective Evolutionary Algorithms: An Overview - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Elaine Guerrero-Pe\u00f1a Aluizio Fausto Ribeiro Ara\u00fajo , &amp; C\u00edcero Garrozi Abstract: Evolutionary algorithms have been widely explored and applied in optimization problems. 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