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Diversity is then exploited to drive different opposing learning strategies at different stages of evolution, thus controlling the exploration and utilization of the algorithm. Finally, SQOBL was embedded in the PSO with eight others representative OBL variants to find the most optimal solution for a test suite. In addition, 8 novel intelligent optimization algorithms and the first three algorithms were selected to evaluate the performance of the latest CEC2022 benchmark test set and realistic constrained optimization problems. Experiments with 56 test functions and 3 real-world constraint optimization problems show that the proposed SQOBL has good integrative properties in CEC2015, CEC2017, CEC2020, and CEC2022 test suites.<\/jats:p>","DOI":"10.1007\/s40747-023-01069-5","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T02:01:30Z","timestamp":1685066490000},"page":"6611-6643","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A particle swarm optimization algorithm based on diversity-driven fusion of opposing phase selection strategies"],"prefix":"10.1007","volume":"9","author":[{"given":"Jiucheng","family":"Xu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2399-7312","authenticated-orcid":false,"given":"Shihui","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Changshun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Ziqin","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"1069_CR1","doi-asserted-by":"publisher","unstructured":"Phan T, Sell D, Wang E-W, Doshay S, Edee K, Yang J, Fan J-A (2019) High-efficiency, large-area, topology-optimized metasurfaces. light-sci appl 8(1):1-9. https:\/\/doi.org\/10.1038\/s41377-019-0159-5","DOI":"10.1038\/s41377-019-0159-5"},{"issue":"2","key":"1069_CR2","doi-asserted-by":"publisher","first-page":"287","DOI":"10.3390\/rs13020287","volume":"13","author":"K Berger","year":"2021","unstructured":"Berger K, Rivera Caicedo J-P, Martino L, Wocher M, Hank T, Verrelst J (2021) A survey of active learning for quantifying vegetation traits from terrestrial earth observation data. 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