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In this paper, an opposition-based evolutionary algorithm with the adaptive clustering mechanism is proposed for solving the complex optimization problem. In particular, opposition-based learning is integrated in the proposed algorithm to initialize the solution, and the nondominated sorting scheme with a new adaptive clustering mechanism is adopted in the environmental selection phase to ensure both convergence and diversity. The proposed method is compared with other nine evolutionary algorithms on a number of test problems with up to fifteen objectives, which verify the best performance of the proposed algorithm. Also, the algorithm is applied to a variety of multiobjective engineering optimization problems. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in solving challenging real-world problems.<\/jats:p>","DOI":"10.1155\/2019\/5126239","type":"journal-article","created":{"date-parts":[[2019,5,2]],"date-time":"2019-05-02T19:35:02Z","timestamp":1556825702000},"page":"1-27","source":"Crossref","is-referenced-by-count":3,"title":["An Opposition-Based Evolutionary Algorithm for Many-Objective Optimization with Adaptive Clustering Mechanism"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1552-5075","authenticated-orcid":true,"given":"Wan Liang","family":"Wang","sequence":"first","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weikun","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, Zhejiang 310023, 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