An adaptive artificial bee colony algorithm for global optimization

Simple item page
dc.contributor.buuauthorYurtkuran, Alkın
dc.contributor.buuauthorEmel, Erdal
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.tr_TR
dc.contributor.orcid0000-0002-9220-7353tr_TR
dc.contributor.orcid0000-0003-2978-2811tr_TR
dc.contributor.researcheridN-8691-2014tr_TR
dc.contributor.researcheridAAH-1410-2021tr_TR
dc.contributor.scopusid26031880400tr_TR
dc.contributor.scopusid6602919521tr_TR
dc.date.accessioned2022-06-06T08:26:07Z
dc.date.available2022-06-06T08:26:07Z
dc.date.issued2015-11-15
dc.description.abstractArtificial bee colony algorithm (ABC) is a recently introduced swarm based meta heuristic algorithm. ABC mimics the foraging behavior of honey bee swarms. Original ABC algorithm is known to have a poor exploitation performance. To remedy this problem, this paper proposes an adaptive artificial bee colony algorithm (AABC), which employs six different search rules that have been successfully used in the literature. Therefore, the AABC benefits from the use of different search and information sharing techniques within an overall search process. A probabilistic selection is applied to deterinine the search rule to be used in generating a candidate solution. The probability of selecting a given search rule is further updated according to its prior performance using the roulette wheel technique. Moreover, a ineinoly length is introduced corresponding to the maximum number of moves to reset selection probabilities. Experiments are conducted using well-known benchmark problems with varying dimensionality to compare AABC with other efficient ABC variants. Computational results reveal that the proposed AABC outperforms other novel ABC variants.en_US
dc.identifier.citationYurtkuran, A. ve Emel, E. (2015). "An adaptive artificial bee colony algorithm for global optimization". Applied Mathematics and Computation, 271, 1004-1023.en_US
dc.identifier.endpage1023tr_TR
dc.identifier.issn0096-3003
dc.identifier.scopus2-s2.0-84944037689tr_TR
dc.identifier.startpage1004tr_TR
dc.identifier.urihttps://doi.org/10.1016/j.amc.2015.09.064
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0096300315013028
dc.identifier.urihttp://hdl.handle.net/11452/26908
dc.identifier.volume271tr_TR
dc.identifier.wos000367819300018tr_TR
dc.indexed.scopusScopusen_US
dc.indexed.wosSCIEen_US
dc.language.isoenen_US
dc.publisherElsevier Scienceen_US
dc.relation.journalApplied Mathematics and Computationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaptive searchen_US
dc.subjectArtificial bee colony algorithmen_US
dc.subjectGlobal optimizationen_US
dc.subjectEfficienten_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectGlobal optimizationen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectOptimizationen_US
dc.subjectAdaptive searchen_US
dc.subjectArtificial bee colony algorithmsen_US
dc.subjectArtificial bee colony algorithms (ABC)en_US
dc.subjectBench-mark problemsen_US
dc.subjectComputational resultsen_US
dc.subjectInformation sharingen_US
dc.subjectMeta heuristic algorithmen_US
dc.subjectSelection probabilitiesen_US
dc.subjectAlgorithmsen_US
dc.subject.scopusBees; Exploration and Exploitation; Coloniesen_US
dc.subject.wosMathematics, applieden_US
dc.titleAn adaptive artificial bee colony algorithm for global optimizationen_US
dc.typeArticle
dc.wos.quartileQ1en_US

Files

License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Web of Science
Scopus