Show simple item record

dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2018-07-19T02:54:51Z
dc.date.available2018-07-19T02:54:51Z
dc.date.issued2016
dc.identifier.issn0941-0643
dc.identifier.doi10.1007/s00521-015-1920-1
dc.identifier.urihttp://hdl.handle.net/10072/101355
dc.description.abstractA novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://www.alimirjalili.com/DA.html.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto21
dc.relation.ispartofjournalNeural Computing and Applications
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchCognitive Sciences
dc.subject.fieldofresearchcode080199
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode1702
dc.titleDragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems
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.authorMirjalili, Seyedali


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record