A Qualified Search Strategy with Artificial Bee Colony Algorithm for Continuous Optimization

Basit Öğe Kaydı
dc.contributor.authorHakli, Huseyin
dc.date.accessioned2024-02-23T14:00:04Z
dc.date.available2024-02-23T14:00:04Z
dc.date.issued2020
dc.departmentNEÜen_US
dc.description.abstractOne of the most popular population-based and swarm intelligence algorithms is the artificial bee colony. Although the ABC method is known for its efficiency in exploration, it has a poor performance in exploitation ability. It uses a single solution search equation that does not provide a balance between exploration and intensification adequately, and this is one of the most common problems in optimization techniques. This study proposes an artificial bee colony algorithm with a qualified search strategy (QSSABC) that uses four different solution search equations to deal with these problems. In order to increase the ability of exploitation, the QSSABC uses the global best solution of population in both of these equations. Equations in the QSSABC method are selected by roulette-wheel method based on their success rates, and equation with the lowest success rate within determined periods is eliminated. The equations' success rates are reset at the end of each period, and it is expected that equations will prove their success again in every period. This qualified search strategy ensures an efficient use of number of function evaluations, and also it achieves balance between global and local search. To evaluate accuracy and performance of the QSSABC, twenty-eight classical functions, twenty-four CEC05 functions and thirty CEC14 functions were used. Simulation results showed that the QSSABC surpasses other methods such as distABC, MABC, ABCVSS in classical functions, and that it is a successful tool for problems with different characteristics by showing better performance over other methods for CEC05 and CEC14 test functions.en_US
dc.identifier.doi10.1007/s13369-020-04875-y
dc.identifier.endpage10913en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85090125606en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage10891en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-020-04875-y
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11443
dc.identifier.volume45en_US
dc.identifier.wosWOS:000565112700004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Bee Colonyen_US
dc.subjectQualified Strategyen_US
dc.subjectSolution Search Equationsen_US
dc.subjectContinuous Optimizationen_US
dc.titleA Qualified Search Strategy with Artificial Bee Colony Algorithm for Continuous Optimizationen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu