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Firstly, L\u2013C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm\u2019s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population\u2019s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm\u2019s advantages in solving complex problems with unknown search spaces.<\/jats:p>","DOI":"10.1007\/s44196-023-00336-0","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T09:02:39Z","timestamp":1695286959000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multi-strategy Improved Seagull Optimization Algorithm"],"prefix":"10.1007","volume":"16","author":[{"given":"Yancang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weizhi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6487-4175","authenticated-orcid":false,"given":"Qiuyu","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huawang","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muxuan","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"336_CR1","doi-asserted-by":"publisher","DOI":"10.1145\/3470971","author":"Y Tian","year":"2021","unstructured":"Tian, Y., Si, L., Zhang, X., Cheng, R., He, C., Chen, T., Jin, Y.: Evolutionary large-scale multi-objective optimization: a survey. 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