Artificial bee colony algorithm with variable search strategy for continuous optimization

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dc.contributor.authorKiran, Mustafa Servet
dc.contributor.authorHakli, Huseyin
dc.contributor.authorGunduz, Mesut
dc.contributor.authorUguz, Harun
dc.date.accessioned2020-03-26T19:00:58Z
dc.date.available2020-03-26T19:00:58Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe artificial bee colony (ABC) algorithm is a swarm-based optimization technique proposed for solving continuous optimization problems. The artificial agents of the ABC algorithm use one solution update rule during the search process. To efficiently solve optimization problems with different characteristics, we propose the integration of multiple solution update rules with ABC in this study. The proposed method uses five search strategies and counters to update the solutions. During initialization, each update rule has a constant counter content. During the search process performed by the artificial agents, these counters are used to determine the rule that is selected by the bees. Because the optimization problems and functions have different characteristics, one or more search strategies are selected and are used during the iterations according to the characteristics of the numeric functions in the proposed approach. By using the search strategies and mechanisms proposed in the present study, the artificial agents learn which update rule is more appropriate based on the characteristics of the problem to find better solutions. The performance and accuracy of the proposed method are examined on 28 numerical benchmark functions, and the obtained results are compared with various classical versions of ABC and other nature-inspired optimization algorithms. The experimental results show that the proposed algorithm, integrated and improved with search strategies, outperforms the basic variants and other variants of the ABC algorithm and other methods in terms of solution quality and robustness for most of the experiments. (C) 2015 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/j.ins.2014.12.043en_US
dc.identifier.endpage157en_US
dc.identifier.issn0020-0255en_US
dc.identifier.issn1872-6291en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage140en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.ins.2014.12.043
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31866
dc.identifier.volume300en_US
dc.identifier.wosWOS:000350192800011en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.ispartofINFORMATION SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial bee colonyen_US
dc.subjectContinuous optimizationen_US
dc.subjectSearch strategyen_US
dc.subjectIntegrationen_US
dc.titleArtificial bee colony algorithm with variable search strategy for continuous optimizationen_US
dc.typeArticleen_US

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