Feature Selection for Metaheuristics Optimization Technique with Chaos

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dc.contributor.authorChaudhury, Sushovan
dc.contributor.authorOyebode, Oluwadare Joshua
dc.contributor.authorNgo Hoang, Dai-Long
dc.contributor.authorRabbi, Fazle
dc.contributor.authorAjibade, Samuel-Soma M.
dc.date.accessioned2023-01-19T11:56:55Z
dc.date.available2023-01-19T11:56:55Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractParticle swarm optimization (PSO) is a global optimization method that is extremely effective. PSO's flaws include slow convergence, premature convergence, and getting stuck at local optima. In this paper, the chaos map and dynamic-weight Particle Swarm Optimization (CPSO) are combined with PSO to improve the search process by adjusting the inertia weight of PSO and changing the position update formula in the Chaos dynamic-weight Particle Swarm Optimization (CPSO), resulting in efficient balancing for local and global PSO feature selection processes. Using eight numerical functions, the performance of CPSO was compared to that of two metaheuristic techniques which are the original PSO and Differential Evolution (DE). The results reveal that the CPSO is an efficient feature selection technique that generates good results by balancing the exploration and exploitation search processes.en_US
dc.identifier.doi10.1109/CSPA55076.2022.9781989en_US
dc.identifier.endpage370en_US
dc.identifier.scopus2-s2.0-85132713537en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage365en_US
dc.identifier.urihttps://hdl.handle.net/11467/6094
dc.identifier.urihttps://doi.org/10.1109/CSPA55076.2022.9781989
dc.identifier.wosWOS:000839140800068en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceedingen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectChaotic map; Differential evolution; Feature selection; Particle swarm optimizationen_US
dc.titleFeature Selection for Metaheuristics Optimization Technique with Chaosen_US
dc.typeConference Objecten_US

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