Galactic Swarm Optimization using Artificial Bee Colony Algorithm

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dc.contributor.authorKaya, Ersin
dc.contributor.authorBabaoglu, Ismail
dc.contributor.authorKodaz, Halife
dc.date.accessioned2020-03-26T19:41:48Z
dc.date.available2020-03-26T19:41:48Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description15th International Conference on ICT and Knowledge Engineering (ICT&KE) -- NOV 22-24, 2017 -- Siam Univ, Bangkok, THAILANDen_US
dc.description.abstractGalactic swarm optimization (GSO) algorithm is a novel meta-heuristic algorithm inspired by the motion of stars, galaxies and superclusters of galaxies under the influence of gravity. The GSO algorithm utilizes multiple cycles of exploration and exploitation in two levels. The first level covers the exploration, and different subpopulations of the candidate solutions are used for exploring the search space. The second level covers the exploitation, and best solutions obtained from the subpopulations are considered as a superswarm and used for exploiting the search space. The first implementation of GSO algorithm was presented by using particle swarm optimization algorithm (PSO) algorithm on both first and second levels. This study presents the preliminary results of an implementation of GSO algorithm by using artificial bee colony (ABC) algorithm on the first level and PSO algorithm on the second level. Due to the better exploration characteristics of ABC algorithm over PSO algorithm, this suggestion covers the usage of ABC algorithm on the first level, and the usage of PSO algorithm on the second level. The proposed approach is tested on 20 well-known online available benchmark problems and preliminary results are presented. According to the experimental results, the proposed approach achieves more successful results than the basic GSO approach.en_US
dc.description.sponsorshipAPD MEN, IEEE, aAwnen_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis study has been supported by Scientific Research Project of Selcuk University.en_US
dc.identifier.endpage28en_US
dc.identifier.isbn978-1-5386-2115-8; 978-1-5386-2117-2
dc.identifier.issn2157-0981en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage23en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/35148
dc.identifier.wosWOS:000426526500004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2017 15TH INTERNATIONAL CONFERENCE ON ICT AND KNOWLEDGE ENGINEERING (ICT&KE)en_US
dc.relation.ispartofseriesInternational Conference on ICT and Knowledge Engineering
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectgalactic swarm optimizationen_US
dc.subjectartificial bee colony algorithmen_US
dc.subjectparticle swarm optimization algorithmen_US
dc.subjectmeta-heuristicen_US
dc.titleGalactic Swarm Optimization using Artificial Bee Colony Algorithmen_US
dc.typeConference Objecten_US

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