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dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorLewis, Andrew
dc.date.accessioned2017-11-30T06:08:25Z
dc.date.available2017-11-30T06:08:25Z
dc.date.issued2016
dc.identifier.issn0965-9978
dc.identifier.doi10.1016/j.advengsoft.2016.01.008
dc.identifier.urihttp://hdl.handle.net/10072/99753
dc.description.abstractThis paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherElsevier
dc.relation.ispartofpagefrom51
dc.relation.ispartofpageto67
dc.relation.ispartofjournalAdvances in Engineering Software
dc.relation.ispartofvolume95
dc.subject.fieldofresearchAnalysis of Algorithms and Complexity
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode080201
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.titleThe Whale Optimization Algorithm
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorLewis, Andrew J.
gro.griffith.authorMirjalili, Seyedali


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