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Some multiobjective brain storm optimization algorithms have low search efficiency. This paper combines the decomposition technology and multiobjective brain storm optimization algorithm (MBSO\/D) to improve the search efficiency. Given weight vectors transform a multiobjective optimization problem into a series of subproblems. The decomposition technology determines the neighboring clusters of each cluster. Solutions of adjacent clusters generate new solutions to update population. An adaptive selection strategy is used to balance exploration and exploitation. Besides, MBSO\/D compares with three efficient state\u2010of\u2010the\u2010art algorithms, e.g., NSGAII and MOEA\/D, on twenty\u2010two test problems. The experimental results show that MBSO\/D is more efficient than compared algorithms and can improve the search efficiency for most test problems.<\/jats:p>","DOI":"10.1155\/2019\/5301284","type":"journal-article","created":{"date-parts":[[2019,1,23]],"date-time":"2019-01-23T04:18:04Z","timestamp":1548217084000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2144-1177","authenticated-orcid":false,"given":"Cai","family":"Dai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9901-1732","authenticated-orcid":false,"given":"Xiujuan","family":"Lei","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,1,22]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2017.2669104"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirp.2008.03.020"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2475755"},{"key":"e_1_2_9_4_2","unstructured":"PalaniappanS. 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