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dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorWang, Gai-Ge
dc.contributor.authorCoelho, Leandro dos S
dc.date.accessioned2017-05-03T16:08:58Z
dc.date.available2017-05-03T16:08:58Z
dc.date.issued2014
dc.identifier.issn0941-0643
dc.identifier.doi10.1007/s00521-014-1629-6
dc.identifier.urihttp://hdl.handle.net/10072/66164
dc.description.abstractThe PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom1423
dc.relation.ispartofpageto1435
dc.relation.ispartofissue6
dc.relation.ispartofjournalNeural Computing and Applications
dc.relation.ispartofvolume25
dc.rights.retentionY
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchComputer vision and multimedia computation
dc.subject.fieldofresearchMachine learning
dc.subject.fieldofresearchcode5204
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode4603
dc.subject.fieldofresearchcode4611
dc.titleBinary optimization using hybrid particle swarm optimization and gravitational search 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.authorMirjalili, Seyedali


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