Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems

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dc.authorscopusid58138730800en_US
dc.authorscopusid7401490255en_US
dc.authorscopusid55354654200en_US
dc.authorscopusid36650599500en_US
dc.authorscopusid57216627885en_US
dc.contributor.authorAbdel-Salam, M.
dc.contributor.authorHu, G.
dc.contributor.authorÇelik, E.
dc.contributor.authorGharehchopogh, F.S.
dc.contributor.authorEL-Hasnony, I.M.
dc.date.accessioned2024-08-23T16:07:39Z
dc.date.available2024-08-23T16:07:39Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced. In addition, the likelihood of falling into local optimal solutions is high, and the convergence speed still needs some work. Hence, there is room for enhancement in the search mechanism so that various search agents can discover new solutions. The authors suggest an adaptive chaotic version of the RIME algorithm named ACRIME, which incorporates four main improvements, including an intelligent population initialization using chaotic maps, a novel adaptive modified Symbiotic Organism Search (SOS) mutualism phase, a novel mixed mutation strategy, and the utilization of restart strategy. The main goal of these improvements is to improve the variety of the population, achieve a better balance between exploration and exploitation, and improve RIME's local and global search abilities. The study assesses the effectiveness of ACRIME by using the standard benchmark functions of the CEC2005 and CEC2019 benchmarks. The proposed ACRIME is also applied as a feature selection to fourteen various datasets to test its applicability to real-world problems. Besides, the ACRIME algorithm is applied to the COVID-19 classification real problem to test its applicability and performance further. The suggested algorithm is compared to other sophisticated classical and advanced metaheuristics, and its performance is assessed using statistical tests such as Wilcoxon rank-sum and Friedman rank tests. The study demonstrates that ACRIME exhibits a high level of competitiveness and often outperforms competing algorithms. It discovers the optimal subset of features, enhancing the accuracy of classification and minimizing the number of features employed. This study primarily focuses on enhancing the equilibrium between exploration and exploitation, extending the scope of local search. © 2024 Elsevier Ltden_US
dc.identifier.doi10.1016/j.compbiomed.2024.108803
dc.identifier.issn0010-4825
dc.identifier.scopus2-s2.0-85197068966en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2024.108803
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14758
dc.identifier.volume179en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChaos theoryen_US
dc.subjectFeature selectionen_US
dc.subjectMetaheuristicsen_US
dc.subjectOptimizationen_US
dc.subjectRIMEen_US
dc.subjectWilcoxon testen_US
dc.subjectChaotic systemsen_US
dc.subjectClassification (of information)en_US
dc.subjectCOVID-19en_US
dc.subjectHeuristic algorithmsen_US
dc.subjectOptimizationen_US
dc.subjectChaoticsen_US
dc.subjectExploration and exploitationen_US
dc.subjectFeatures selectionen_US
dc.subjectLocal searchen_US
dc.subjectMetaheuristicen_US
dc.subjectOptimisationsen_US
dc.subjectOptimization algorithmsen_US
dc.subjectPerformanceen_US
dc.subjectRIMEen_US
dc.subjectWilcoxon testen_US
dc.subjectFeature Selectionen_US
dc.titleChaotic RIME optimization algorithm with adaptive mutualism for feature selection problemsen_US
dc.typeArticleen_US

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