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Evol. Learn. Optim."],"published-print":{"date-parts":[[2023,12,31]]},"abstract":"<jats:p>Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm\u2019s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method.<\/jats:p>","DOI":"10.1145\/3604812","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T11:26:16Z","timestamp":1686741976000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6585-9850","authenticated-orcid":false,"given":"Delaram","family":"Yazdani","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Mashhad Branch, Azad University, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7799-5013","authenticated-orcid":false,"given":"Danial","family":"Yazdani","sequence":"additional","affiliation":[{"name":"Faculty of Engineering &amp; Information Technology, University of Technology Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2151-0547","authenticated-orcid":false,"given":"Donya","family":"Yazdani","sequence":"additional","affiliation":[{"name":"AI Lab, British Antarctic Survey, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1944-4624","authenticated-orcid":false,"given":"Mohammad Nabi","family":"Omidvar","sequence":"additional","affiliation":[{"name":"School of Computing and Leeds University Business School, University of Leeds, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2798-0104","authenticated-orcid":false,"given":"Amir H.","family":"Gandomi","sequence":"additional","affiliation":[{"name":"Faculty of Engineering &amp; Information Technology, University of Technology Sydney, Australia, and University Research and Innovation Center (EKIK), Obuda University, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8837-4442","authenticated-orcid":false,"given":"Xin","family":"Yao","sequence":"additional","affiliation":[{"name":"Research Institute of Trustworthy Autonomous Systems (RITAS), and Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, China and The Center of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, University of Birmingham, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"e_1_3_2_2_1","first-page":"1","volume-title":"IEEE Congress on Evolutionary Computation","author":"Amo Ignacio G. 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