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Many researches have shown that FBI is a promising algorithm due to two specific population types. However, there is no sufficient information exchange between these two population types in the original FBI algorithm. Therefore, FBI suffers from many problems. This paper incorporates a novel self-adaptive population control strategy into FBI algorithm to adjust parameters based on the fitness transformation from the previous iteration, named SaFBI. In addition to the self-adaptive mechanism, our proposed SaFBI refers to a novel updating operator to further improve the robustness and effectiveness of the algorithm. To prove the availability of the proposed algorithm, we select 51 CEC benchmark functions and two well-known engineering problems to verify the performance of SaFBI. Experimental and statistical results manifest that the proposed SaFBI algorithm performs superiorly compared to some state-of-the-art algorithms.<\/jats:p>","DOI":"10.1007\/s44196-023-00396-2","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T09:02:51Z","timestamp":1706086971000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Self-Adaptive Forensic-Based Investigation Algorithm with Dynamic Population for Solving Constraint Optimization Problems"],"prefix":"10.1007","volume":"17","author":[{"given":"Pengxing","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Todo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5042-3261","authenticated-orcid":false,"given":"Shangce","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"issue":"10","key":"396_CR1","doi-asserted-by":"publisher","first-page":"4286","DOI":"10.1109\/TFUZZ.2022.3146986","volume":"30","author":"C Pozna","year":"2022","unstructured":"Pozna, C., Precup, R.E., Horv\u00e1th, E., Petriu, E.M.: Hybrid particle filter-particle swarm optimization algorithm and application to fuzzy controlled servo systems. 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Author names: Pengxing Cai, Yu Zhang, Ting Jin, Yuki Todo, and Shangce Gao","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We understand that the Corresponding Author is the sole contact for the Editorial process. He\/she is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. Author names: Pengxing Cai, Yu Zhang, Ting Jin, Yuki Todo, and Shangce Gao","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Author Agreement Statement"}},{"value":"No human or animal subjects were involved in this experiment.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"No human subjects were involved in this experiment.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}],"article-number":"15"}}