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dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorMirjalili, Seyed Mohammad
dc.contributor.authorLewis, Andrew
dc.date.accessioned2018-09-28T00:37:45Z
dc.date.available2018-09-28T00:37:45Z
dc.date.issued2014
dc.identifier.issn0965-9978
dc.identifier.doi10.1016/j.advengsoft.2013.12.007
dc.identifier.urihttp://hdl.handle.net/10072/66188
dc.description.abstractThis work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherPergamon Press
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofpagefrom46
dc.relation.ispartofpageto61
dc.relation.ispartofjournalAdvances in Engineering Software
dc.relation.ispartofvolume69
dc.rights.retentionY
dc.subject.fieldofresearchOptimisation
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode490304
dc.subject.fieldofresearchcode46
dc.subject.fieldofresearchcode40
dc.titleGrey wolf optimizer
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2014 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorLewis, Andrew J.
gro.griffith.authorMirjalili, Seyedali


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