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Nonetheless, conventional training algorithms such as backpropagation encounter limitations, including getting trapped in sub-optimal solutions. To rectify these inadequacies, metaheuristic population algorithms are advocated as a dependable alternative. In this paper, we introduce a novel training methodology termed, DDE-OP, which leverages the principles of differential evolution enriched with a division-based scheme and an opposite-direction strategy. Our approach integrates two effective concepts with differential evolution. Initially, the proposed algorithm identifies partitions within the search space through a clustering algorithm and designates the obtained cluster centres to serve as representatives. Subsequently, an updating scheme incorporates these clusters into the current population. Lastly, a quasi-opposite-direction strategy is used to augment search space exploration. Extensive evaluation on diverse classification and approximation tasks demonstrate that DDE-OP surpasses conventional and population-based methodologies.<\/jats:p>","DOI":"10.1007\/s12530-024-09641-1","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T12:50:54Z","timestamp":1733403054000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A novel metaheuristic population algorithm for optimising the connection weights of neural networks"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8661-7578","authenticated-orcid":false,"given":"Seyed Jalaleddin","family":"Mousavirad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerald","family":"Schaefer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khosro","family":"Rezaee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diego","family":"Oliva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Davood","family":"Zabihzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ripon K.","family":"Chakrabortty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamzeh","family":"Mohammadigheymasi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehdi","family":"Pedram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"9641_CR1","doi-asserted-by":"crossref","unstructured":"Abusnaina AA, Ahmad S, Jarrar R, Mafarja M (2018) Training neural networks using salp swarm algorithm for pattern classification. 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