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However, this Roulette wheel\u2010based global particle selection is ineffective for convergence and diversity when the problem has numerous decision variables or a large number of global\u2010best candidates. Thus, this study proposes the cluster\u2010based MOPSO (CMOPSO). In CMOPSO, the similarities between particles are considered when selecting the global\u2010best particle. The cluster for each particle is determined based on the Euclidean distance in the decision or objective space. The proposed approach is demonstrated by applying an operating condition optimization problem to the hydrogen production process. The target process is a representative chemical plant with a large search space and strong nonlinearity. Furthermore, the performance of CMOPSO is assessed by comparing it with that of MOPSO. The results indicate that CMOPSO considered in the decision space exhibits superior performance in terms of convergence and diversity.<\/jats:p>","DOI":"10.1155\/2023\/5275262","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T13:35:05Z","timestamp":1677591305000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Cluster\u2010Based Multiobjective Particle Swarm Optimization and Application for Chemical Plants"],"prefix":"10.1155","volume":"2023","author":[{"given":"Seokyoung","family":"Hong","sequence":"first","affiliation":[]},{"given":"Jaewon","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Hyungtae","family":"Cho","sequence":"additional","affiliation":[]},{"given":"Kyojin","family":"Jang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2311-4567","authenticated-orcid":false,"given":"Junghwan","family":"Kim","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2013.2240688"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9378-3"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1162\/evco.1994.2.3.221"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2011.08.745"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.797969"},{"key":"e_1_2_9_7_2","first-page":"95","article-title":"SPEA2 improving the strength pareto evolutionary algorithm","volume":"2","author":"Zitzler E.","year":"2001","journal-title":"Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2016.03.038"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfl253"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"KimY. 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