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dc.contributor.authorSaremi, Shahrzad
dc.contributor.authorMirjalili, Seyedeh Zahra
dc.contributor.authorMirjalili, Seyed Mohammad
dc.date.accessioned2018-07-19T05:13:39Z
dc.date.available2018-07-19T05:13:39Z
dc.date.issued2015
dc.identifier.issn0941-0643
dc.identifier.doi10.1007/s00521-014-1806-7
dc.identifier.urihttp://hdl.handle.net/10072/103356
dc.description.abstractEvolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO–EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer UK
dc.publisher.placeUnited Kingdom
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto7
dc.relation.ispartofjournalNeural Computing and Applications
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchCognition
dc.subject.fieldofresearchcode5204
dc.subject.fieldofresearchcode520401
dc.titleEvolutionary population dynamics and grey wolf optimizer
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.
gro.hasfulltextNo Full Text
gro.griffith.authorSaremi, Shahrzad


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