A novel modified bat algorithm hybridizing by differential evolution algorithm

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dc.contributor.authorYlidizdan, Gulnur.
dc.contributor.authorBaykan, Omer Kaan.
dc.date.accessioned2020-03-26T20:20:05Z
dc.date.available2020-03-26T20:20:05Z
dc.date.issued2020
dc.departmentSelçuk Üniversitesi, Kulu Meslek Yüksekokulu, Bigisayar Teknolojileri Bölümüen_US
dc.description.abstractThe bat algorithm (BA) is one of the metaheuristic algorithms that are used to solve optimization problems. The differential evolution (DE) algorithm is also applied to optimization problems and has successful exploitation ability. In this study, an advanced modified BA (MBA) algorithm was initially proposed by making some modifications to improve the exploration and exploitation abilities of the BA. A hybrid system (MBADE), involving the use of the MBA in conjunction with the DE, was then suggested in order to further improve the exploitation potential and provide superior performance in various test problem clusters. The proposed hybrid system uses a common population, and the algorithm to be applied to the individual is selected on the basis of a probability value, which is calculated in accordance with the performance of the algorithms; thus, the probability of applying a successful algorithm is increased. The performance of the proposed method was tested on functions that have frequently been studied, such as classical benchmark functions, small-scale CEC 2005 benchmark functions, large-scale CEC 2010 benchmark functions, and CEC 2011 real-world problems. The obtained results were compared with the results obtained from the standard BA and other findings in the literature and interpreted by means of statistical tests. The developed hybrid system showed superior performance to the standard BA in all test problem sets and produced more acceptable results when compared to the published data for the existing algorithms. In addition, the contribution of the MBA and DE algorithms to the hybrid system was examined. (C) 2019 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationYildizdan, G., Baykan, Ö. K. (2020). A Novel Modified Bat Algorithm Hybridizing by Differential Evolution Algorithm. Expert Systems with Applications, 141, 1-19.
dc.identifier.doi10.1016/j.eswa.2019.112949en_US
dc.identifier.endpage19
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2019.112949
dc.identifier.urihttps://hdl.handle.net/20.500.12395/38498
dc.identifier.volume141en_US
dc.identifier.wosWOS:000496334800024en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYlidizdan, Gulnur.
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectBat algorithmen_US
dc.subjectDifferential evolution algorithmen_US
dc.subjectContinuous optimizationen_US
dc.subjectLarge-scale optimizationen_US
dc.titleA novel modified bat algorithm hybridizing by differential evolution algorithmen_US
dc.typeArticleen_US

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