Improvement of Population Diversity of Meta-heuristics Algorithm Using Chaotic Map

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorOgunbolu, Mary O.
dc.contributor.authorChweya, Ruth
dc.contributor.authorFadipe, Samuel
dc.date.accessioned2023-01-18T11:57:13Z
dc.date.available2023-01-18T11:57:13Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractParticle swarm optimization (PSO) is a global optimization and nature-inspired algorithm known for its good quality and easily applied in various real-world optimization challenges. Nevertheless, PSO has some weaknesses such as slow convergence, converging prematurely and simply gets stuck at local optima. This study aims to solve the problem of deprived population diversity in the search process of PSO which causes premature convergence. Therefore, in this research, a method is brought to PSO to keep away from early stagnation which explains premature convergence. The aim of this research is to propose a chaotic dynamic weight particle swarm optimization (CHPSO) wherein a chaotic logistic map is utilized to enhance the populace diversity within the search technique of PSO with the aid of editing the inertia weight of PSO in an effort to avoid premature convergence. This study additionally investigates the overall performance and feasibility of the proposed CHPSO as a function selection set of rules for fixing problems of optimization. 8 benchmark functions had been used to assess the overall performance and seek accuracy of the proposed (CHPSO) algorithms and as compared with a few other meta-heuristics optimization set of rules. The outcomes of the experiments show that the CHPSO achieves correct consequences in fixing an optimization and has established to be a dependable and green metaheuristics algorithm for selection of features.en_US
dc.identifier.doi10.1007/978-3-030-98741-1_9en_US
dc.identifier.scopus2-s2.0-85127889073en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6063
dc.identifier.urihttps://doi.org/10.1007/978-3-030-98741-1_9
dc.identifier.wosWOS:000783765200009en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes on Data Engineering and Communications Technologiesen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInertia weight; Particle swarm optimization; Poor diversity; Population diversity; Premature convergenceen_US
dc.titleImprovement of Population Diversity of Meta-heuristics Algorithm Using Chaotic Mapen_US
dc.typeBook Chapteren_US

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