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dc.contributor.authorMirjalili, Seyedeh Zahra
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorZhang, Hongyu
dc.contributor.authorChalup, Stephan
dc.contributor.authorNoman, Nasimul
dc.date.accessioned2020-06-07T23:46:12Z
dc.date.available2020-06-07T23:46:12Z
dc.date.issued2019
dc.identifier.issn2210-6502
dc.identifier.doi10.1016/j.swevo.2019.100579
dc.identifier.urihttp://hdl.handle.net/10072/394459
dc.description.abstractIn the field of robust optimization, the robustness of a solution is confirmed using a robustness indicator. In the literature, such an indicator uses explicit or implicit averaging techniques. One of the main drawbacks of the implicit averaging techniques is unreliability since they only use the sampled points generated by an optimization algorithm. In this paper, we propose a set of conditional operators for comparing solutions based on the number of sampled solutions in their neighbourhoods, thereby making reliable decisions during the process of robust optimization. This technique is integrated into the Particle Swarm Optimization (PSO) to update GBEST and PBESTs reliably, and the designed robust PSO algorithm is applied to a number of case studies. A set of extensive experiments shows that the proposed technique prevents an algorithm that relies on implicit averaging technique from making risky decisions and thus proven beneficial in finding robust solutions.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofjournalSwarm and Evolutionary Computation
dc.relation.ispartofvolume51
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.subject.keywordsScience & Technology
dc.subject.keywordsTechnology
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.subject.keywordsComputer Science, Theory & Methods
dc.subject.keywordsComputer Science
dc.titleImproving the reliability of implicit averaging methods using new conditional operators for robust optimization
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationMirjalili, SZ; Mirjalili, S; Zhang, H; Chalup, S; Noman, N, Improving the reliability of implicit averaging methods using new conditional operators for robust optimization, Swarm and Evolutionary Computation, 2019, 51
dc.date.updated2020-06-07T23:42:38Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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