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In this paper, TS\/KW-MaOEA is proposed for solving many-objective optimization problems (MaOPs), which keeps TS as the central and equips a perfect control mechanism for separated balance. More specifically, TS\/KW-MaOEA can automatically adjust the balance trend and provide appropriate selection pressure for MaOPs according to the Kondratiev wave (KW) search model and the objective space dimension. To verify the effectiveness of the proposed algorithm, a series of experiments are carried out against seven state-of-the-art many-objective optimization algorithms on 15 benchmark problems with up to 30 objectives. Experimental results indicate that the proposed algorithm is highly competitive against peer competitors.<\/jats:p>","DOI":"10.1007\/s40747-024-01505-0","type":"journal-article","created":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T08:02:37Z","timestamp":1718438557000},"page":"6509-6543","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Two-stage many-objective evolutionary algorithm: enhanced dominance relations and control mechanisms for separated balance"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4336-5582","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"first","affiliation":[]},{"given":"Qilin","family":"Niliang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qiaoyong","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,15]]},"reference":[{"key":"1505_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-0-85729-652-8_1","volume-title":"Multi-objective evolutionary optimisation for product design and manufacturing","author":"K Deb","year":"2011","unstructured":"Deb K (2011) Multi-objective optimisation using evolutionary algorithms: an introduction. 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