An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization

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dc.contributor.authorHakli, Huseyin
dc.contributor.authorKiran, Mustafa Servet
dc.date.accessioned2024-02-23T13:59:59Z
dc.date.available2024-02-23T13:59:59Z
dc.date.issued2020
dc.departmentNEÜen_US
dc.description.abstractThe artificial bee colony, ABC for short, algorithm is population-based iterative optimization algorithm proposed for solving the optimization problems with continuously-structured solution space. Although ABC has been equipped with powerful global search capability, this capability can cause poor intensification on found solutions and slow convergence problem. The occurrence of these issues is originated from the search equations proposed for employed and onlooker bees, which only updates one decision variable at each trial. In order to address these drawbacks of the basic ABC algorithm, we introduce six search equations for the algorithm and three of them are used by employed bees and the rest of equations are used by onlooker bees. Moreover, each onlooker agent can modify three dimensions or decision variables of a food source at each attempt, which represents a possible solution for the optimization problems. The proposed variant of ABC algorithm is applied to solve basic, CEC2005, CEC2014 and CEC2015 benchmark functions. The obtained results are compared with results of the state-of-art variants of the basic ABC algorithm, artificial algae algorithm, particle swarm optimization algorithm and its variants, gravitation search algorithm and its variants and etc. Comparisons are conducted for measurement of the solution quality, robustness and convergence characteristics of the algorithms. The obtained results and comparisons show the experimentally validation of the proposed ABC variant and success in solving the continuous optimization problems dealt with the study.en_US
dc.identifier.doi10.1007/s13042-020-01094-7
dc.identifier.endpage2076en_US
dc.identifier.issn1868-8071
dc.identifier.issn1868-808X
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85080024732en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2051en_US
dc.identifier.urihttps://doi.org/10.1007/s13042-020-01094-7
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11407
dc.identifier.volume11en_US
dc.identifier.wosWOS:000515959300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofInternational Journal Of Machine Learning And Cyberneticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Bee Colonyen_US
dc.subjectContinuous Optimizationen_US
dc.subjectNumeric Functionen_US
dc.subjectSearch Strategyen_US
dc.titleAn improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimizationen_US
dc.typeArticleen_US

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