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Self-adaptive particle swarm optimization (SAPSO) algorithms attempt to adjust control parameters during the optimization process, ideally without introducing additional control parameters to which the performance is sensitive. This paper proposes a belief space (BS) approach, borrowed from cultural algorithms (CAs), towards development of a SAPSO. The resulting BS-SAPSO utilizes a belief space to direct the search for optimal control parameter values by excluding non-promising configurations from the control parameter space. The resulting BS-SAPSO achieves an improvement in performance of 3\u201355% above the various baselines, based on the solution quality of the objective function values achieved on the functions tested.<\/jats:p>","DOI":"10.1007\/s11721-023-00232-5","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T06:02:25Z","timestamp":1706680945000},"page":"31-78","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Belief space-guided approach to self-adaptive particle swarm optimization"],"prefix":"10.1007","volume":"18","author":[{"given":"Daniel von","family":"Eschwege","sequence":"first","affiliation":[]},{"given":"Andries","family":"Engelbrecht","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"232_CR1","unstructured":"Beielstein, T., Parsopoulos, K. E., & Vrahatis, M. N. (2002). 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