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dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorLewis, Andrew
dc.contributor.authorMostaghim, Sanaz
dc.date.accessioned2017-11-30T06:12:52Z
dc.date.available2017-11-30T06:12:52Z
dc.date.issued2015
dc.identifier.issn0020-0255
dc.identifier.doi10.1016/j.ins.2015.04.010
dc.identifier.urihttp://hdl.handle.net/10072/104586
dc.description.abstractIn meta-heuristic optimisation, the robustness of a particular solution can be confirmed by re-sampling, which is reliable but computationally expensive, or by reusing neighbourhood solutions, which is cheap but unreliable. This work proposes a novel metric called the confidence measure to increase the reliability of the latter method, defines new confidence-based operators for robust meta-heuristics, and establishes a new robust optimisation approach called confidence-based robust optimisation. The confidence metric and five confidence-based operators are proposed and employed to design two new meta-heuristics: confidence-based robust Particle Swarm Optimisation and confidence-based robust Genetic Algorithm. A set of fifteen robust benchmark problems is employed to investigate the efficiencies of the proposed algorithms. The results show that the proposed metric is able to calculate the confidence level of solutions effectively during the optimisation process. In addition, the results demonstrate that the proposed operators can be employed to design a confident robust optimisation process and are readily applicable to different meta-heuristics.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeUnited States
dc.relation.ispartofpagefrom114
dc.relation.ispartofpageto142
dc.relation.ispartofjournalInformation Sciences
dc.relation.ispartofvolume317
dc.subject.fieldofresearchNeural, Evolutionary and Fuzzy Computation
dc.subject.fieldofresearchOptimisation
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode080108
dc.subject.fieldofresearchcode010303
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.titleConfidence measure: A novel metric for robust meta-heuristic optimisation algorithms
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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