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The reported computational results showed that the proposed RLAM is efficient and effective and that the proposed RLPSO is superior to several state-of-the-art PSO variants.<\/jats:p>","DOI":"10.1007\/s40747-023-01012-8","type":"journal-article","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T04:25:26Z","timestamp":1679718326000},"page":"5585-5609","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Reinforcement-learning-based parameter adaptation method for particle swarm optimization"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9437-7807","authenticated-orcid":false,"given":"Shiyuan","family":"Yin","sequence":"first","affiliation":[]},{"given":"Min","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Huaxiang","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Guoliang","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Wenyu","family":"Mao","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Wenchang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,25]]},"reference":[{"key":"1012_CR1","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.swevo.2019.04.008","volume":"48","author":"J Del Ser","year":"2019","unstructured":"Del Ser J, Osaba E, Molina D, Yang X-S, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello Coello CA, Herrera F (2019) Bio-inspired computation: Where we stand and what\u2019s next. 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