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To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions.<\/jats:p>","DOI":"10.1007\/s40747-022-00824-4","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T11:13:33Z","timestamp":1659006813000},"page":"659-675","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems"],"prefix":"10.1007","volume":"9","author":[{"given":"Meirong","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yinan","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yaochu","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Shengxiang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Dunwei","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Zekuan","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"issue":"1","key":"824_CR1","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/978-3-540-70928-2_60","volume":"4403","author":"K Deb","year":"2007","unstructured":"Deb K, Udaya BRN, Karthik S (2007) Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. 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