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Imperialist competitive algorithm (ICA) and sine cosine algorithm (SCA) are two potential algorithms for handling single-objective optimization problems, but the research of them in multiobjective optimization is scarce. In this paper, a fusion multiobjective empire split algorithm (FMOESA) is proposed. First, an initialization operation based on opposition-based learning strategy is hired to generate a good initial population. A new reproduction of offspring is introduced, which combines ICA and SCA. Besides, a novel power evaluation mechanism is proposed to identify individual performance, which takes into account both convergence and diversity of population. Experimental studies on several benchmark problems show that FMOESA is competitive compared with the state-of-the-art algorithms. Given both good performance and nice properties, the proposed algorithm could be an alternative tool when dealing with multiobjective optimization problems.<\/jats:p>","DOI":"10.1155\/2020\/8882086","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T03:35:08Z","timestamp":1608003308000},"page":"1-14","source":"Crossref","is-referenced-by-count":0,"title":["A Fusion Multiobjective Empire Split Algorithm"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-3440-054X","authenticated-orcid":true,"given":"Liang","family":"Liang","sequence":"first","affiliation":[{"name":"School of Accounting College, Zibo Vocational Institute, Zibo 255000, Shandong, China"}]}],"member":"98","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1124-0_66"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/bf00175354"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1023\/a:1008202821328"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-32494-1_4"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/ci-m.2006.248054"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/mhs.1995.494215"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-017-2894-y"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2018.12.001"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.04.110"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22723-4_5"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"12","article-title":"SPEA2: Improving the strength pareto evolutionary algorithm","author":"E. 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