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To solve MMOPs, this paper proposes an evolutionary algorithm with clustering\u2010based assisted selection strategy for multimodal multiobjective optimization, in which the addition operator and deletion operator are proposed to comprehensively consider the diversity in both decision and objective spaces. Specifically, in decision space, the union population is partitioned into multiple clusters by using a density\u2010based clustering method, aiming to assist the addition operator to strengthen the population diversity. Then, a number of weight vectors are adopted to divide population into <jats:italic>N<\/jats:italic> subregions in objective space (<jats:italic>N<\/jats:italic> is population size). Moreover, in the deletion operator, the solutions in the most crowded subregion are first collected into previous clusters, and then the worst solution in the most crowded cluster is deleted until there are <jats:italic>N<\/jats:italic> solutions left. Our algorithm is compared with other multimodal multiobjective evolutionary algorithms on the well\u2010known benchmark MMOPs. Numerical experiments report the effectiveness and advantages of our proposed algorithm.<\/jats:p>","DOI":"10.1155\/2021\/4393818","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T20:39:29Z","timestamp":1610570369000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Evolutionary Algorithm with Clustering\u2010Based Assisted Selection Strategy for Multimodal Multiobjective Optimization"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4554-2677","authenticated-orcid":false,"given":"Naili","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2271-7673","authenticated-orcid":false,"given":"Wu","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3318-2173","authenticated-orcid":false,"given":"Peizhi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,1,13]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2015.2476796"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2788397"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/5695917"},{"key":"e_1_2_10_4_2","volume-title":"Multi-Objective Optimization Using Evolutionary Algorithms","author":"Deb K.","year":"2001"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2856832"},{"key":"e_1_2_10_6_2","volume-title":"Evolutionary Algorithms for Solving Multi-Objective Problems","author":"Coello C.","year":"2007"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/9147649"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/9643273"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/7436712"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"e_1_2_10_11_2","unstructured":"ZitzlerE. 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