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Hybridization of Particle Swarm Optimization with adaptive genetic algorithm operators

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dc.contributor.authorMasrom, Suraya
dc.contributor.authorMoser, Irene
dc.contributor.authorMontgomery, James
dc.contributor.authorAbidin, Siti Zaleha Zainal
dc.contributor.authorOmar, Nasiroh
dc.coverage.spatialTBC
dc.date.accessioned2015-12-13T22:36:04Z
dc.date.created8 December 2013 through 10 December 2013
dc.date.issued2014
dc.date.updated2015-12-11T09:29:58Z
dc.description.abstractParticle Swarm Optimization (PSO) is a popular algorithm used extensively in continuous optimization. One of its well-known drawbacks is its propensity for premature convergence. Many techniques have been proposed for alleviating this problem. One of the
dc.identifier.isbn9781479935161
dc.identifier.urihttp://hdl.handle.net/1885/76615
dc.publisherIEEE
dc.relation.ispartofseries2013 13th International Conference on Intellient Systems Design and Applications, ISDA 2013
dc.sourceInternational Conference on Intelligent Systems Design and Applications, ISDA
dc.titleHybridization of Particle Swarm Optimization with adaptive genetic algorithm operators
dc.typeConference paper
local.bibliographicCitation.lastpage158
local.bibliographicCitation.startpage153
local.contributor.affiliationMasrom, Suraya, Universiti Teknologi MARA
local.contributor.affiliationMoser, Irene, Swinburne University
local.contributor.affiliationMontgomery, James, College of Engineering and Computer Science, ANU
local.contributor.affiliationAbidin, Siti Zaleha Zainal, Universiti Teknologi MARA
local.contributor.affiliationOmar, Nasiroh, Universiti Teknologi MARA
local.contributor.authoruidMontgomery, James, u5072917
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080108 - Neural, Evolutionary and Fuzzy Computation
local.identifier.ariespublicationU3488905xPUB5418
local.identifier.doi10.1109/ISDA.2013.6920726
local.identifier.scopusID2-s2.0-84908178965
local.type.statusPublished Version

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