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

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dc.authoridhttps://orcid.org/0000-0002-5229-4018en_US
dc.authoridhttps://orcid.org/0000-0002-7687-9061en_US
dc.authoridhttps://orcid.org/0000-0002-2481-0230en_US
dc.authorscopusid36728602600en_US
dc.authorscopusid36705310900en_US
dc.authorscopusid57188968069en_US
dc.authorwosidG-2829-2015en_US
dc.authorwosidD-7354-2015en_US
dc.authorwosidABH-7309-2020en_US
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.issued2022en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
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 convergenceen_US
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.2022.11693en_US
dc.identifier.doi10.1016/j.eswa.2022.116930en_US
dc.identifier.endpage14en_US
dc.identifier.scopus2-s2.0-85126871245en_US
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.wos000858787700001en_US
dc.identifier.wosqualityQ1en_US
dc.institutionauthorÜstün, Deniz
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectArtificial bee colony (ABC)en_US
dc.subjectDifferential evolution (DE)en_US
dc.subjectOptimization algorithmen_US
dc.subjectModified ABCen_US
dc.subjectModified algorithmen_US
dc.subjectHybrid algorithmen_US
dc.titleModified artificial bee colony algorithm with differential evolution to enhance precision and convergence performanceen_US
dc.typearticleen_US

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