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Coarse-grained parallel evolution of subpopulations in <jats:italic>mainPop<\/jats:italic> and guidance information provided by <jats:italic>helpPop<\/jats:italic> can facilitate <jats:italic>mainPop<\/jats:italic> to quickly approach the Pareto front. In the second stage, the objective function of <jats:italic>mainPop<\/jats:italic> changes to the original problem. Coevolution of <jats:italic>mainPop<\/jats:italic> and <jats:italic>helpPop<\/jats:italic> by sharing offsprings can produce solutions with better diversity. In the third stage, the mining of the global optimal solutions is performed, discarding <jats:italic>helpPop<\/jats:italic> to save computational resources. For CMOEA-TMC, the combination of parallel evolution, coevolution, and staging strategy makes it easier for <jats:italic>mainPop<\/jats:italic> to converge and maintain good diversity. Experimental results on 33 benchmark CMOPs and a real-world boiler combustion optimization case show that CMOEA-TMC is more competitive than the other five advanced CMOEAs.<\/jats:p>","DOI":"10.1007\/s40747-023-01181-6","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T09:01:56Z","timestamp":1690794116000},"page":"655-675","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A novel three-stage multi-population evolutionary algorithm for constrained multi-objective optimization problems"],"prefix":"10.1007","volume":"10","author":[{"given":"Chenli","family":"Shi","sequence":"first","affiliation":[]},{"given":"Ziqi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaohang","family":"Jin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0599-7324","authenticated-orcid":false,"given":"Zhengguo","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Zhangsheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"issue":"2","key":"1181_CR1","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/TEVC.2003.810761","volume":"7","author":"PA Bosman","year":"2003","unstructured":"Bosman PA, Thierens D (2003) The balance between proximity and diversity in multiobjective evolutionary algorithms. 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