Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance

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dc.authoridhttps://orcid.org/0000-0002-5229-4018
dc.authoridhttps://orcid.org/0000-0002-7687-9061
dc.authoridhttps://orcid.org/0000-0002-2481-0230
dc.authorscopusid36728602600
dc.authorscopusid36705310900
dc.authorscopusid57188968069
dc.authorwosidG-2829-2015
dc.authorwosidD-7354-2015
dc.authorwosidABH-7309-2020
dc.contributor.authorÜstün, Deniz
dc.contributor.authorToktaş, Abdurrahim
dc.contributor.authorErkan, Uğur
dc.contributor.authorAkdağlı, Ali
dc.date.accessioned2024-07-31T13:12:10Z
dc.date.available2024-07-31T13:12:10Z
dc.date.issued2022
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractArtificial bee colony (ABC) and differential evolution (DE) are the most powerful and operative meta-heuristic algorithms inspired by the nature. Although both algorithms are successful, their successes vary from phase to phase, i.e. while ABC is better in the exploration ability, DE is well in the exploitation capability. Because the diversity of mutation and exponential crossover operators is prominently better than that of onlooker bee; in this study, the exploitation ability of ABC is enhanced by replacing the onlooker bee operator with those of mutation and the crossover phases of DE in order to increase the accuracy and speed up the convergence. We hereby introduce a novel modified algorithm denoted “modified ABC by DE” (mABC). The precision performance of mABC is verified through 20 classical benchmark functions and CEC 2014 test suit by a comprehensive comparison with recent ABC variants and hybrids for 30 and 50 dimensions. The results are interpreted using various statistical evaluations such as Wilcoxon, Friedman, and Nemenyi tests. Moreover, mABC is comparatively examined over convergence plots. In concise, the mean ranks of mABC are 1.4 and 2.3 for classical benchmark functions and CEC 2014, respectively. mABC outperforms the other variants averagely for 14 of 20 classical benchmark functions and 24 of 30 CEC 2014 functions. The results manifest that the proposed mABC is a robust and reliable algorithm as well as better than the existing ABC variants and hybrids with regard to high optimization performance like precision and convergence
dc.identifier.citationUstun D., Toktas A., Erkan U., ve Akdagli A.(2022). Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance, Expert Systems with Applications, 198. DOI: 10.1016/j.eswa.2022.11693
dc.identifier.doi10.1016/j.eswa.2022.116930
dc.identifier.endpage14en_US
dc.identifier.scopus2-s2.0-85126871245
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.116930
dc.identifier.urihttps://hdl.handle.net/20.500.13099/306
dc.identifier.volume198en_US
dc.identifier.wosWOS:000858787700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÜstün, Deniz
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofExpert Systems with Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectArtificial bee colony (ABC)
dc.subjectDifferential evolution (DE)
dc.subjectOptimization algorithm
dc.subjectModified ABC
dc.subjectModified algorithm
dc.subjectHybrid algorithm
dc.titleModified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance
dc.typeArticle

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