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dc.contributor.authorMirjalili, SZ
dc.contributor.authorChalup, S
dc.contributor.authorMirjalili, S
dc.contributor.authorNoman, N
dc.date.accessioned2021-01-18T01:31:50Z
dc.date.available2021-01-18T01:31:50Z
dc.date.issued2020
dc.identifier.isbn9781728169293
dc.identifier.doi10.1109/CEC48606.2020.9185748
dc.identifier.urihttp://hdl.handle.net/10072/401213
dc.description.abstractRobust optimization of real-world problems is essential to reduce the significant negative impact of uncertainties and noises present in the environment. Uncertainties in the decision variables are often handled using explicit or implicit averaging methods, in which the fitness of a solution isjudged based on the objective values of neighbouring solutions. Explicit averaging methods are highly reliable but require additional objective function evaluation, which can significantly increases the overall computational cost of an optimization process. On the other hand, implicit averaging techniques are computationally cheap, yet they suffer from low reliability since they use the history of search in a population-based optimization algorithm. This work proposes a conditional Pareto optimal dominance to improve the reliability of robust optimization methods that use implicit averaging methods. The proposed method is applied to Multi-Objective Particle Swarm optimisation. Empirical study with a benchmark suite shows the benefit of the proposed conditional Pareto optimal dominance in locating robust solutions in multi-objective problems.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020)
dc.relation.ispartofconferencetitle2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
dc.relation.ispartofdatefrom2020-07-19
dc.relation.ispartofdateto2020-07-24
dc.relation.ispartoflocationGlasgow, United Kingdom
dc.subject.fieldofresearchEvolutionary computation
dc.subject.fieldofresearchFuzzy computation
dc.subject.fieldofresearchProcedural content generation
dc.subject.fieldofresearchDeep learning
dc.subject.fieldofresearchNeural networks
dc.subject.fieldofresearchTheory of computation
dc.subject.fieldofresearchcode460203
dc.subject.fieldofresearchcode460204
dc.subject.fieldofresearchcode460705
dc.subject.fieldofresearchcode461103
dc.subject.fieldofresearchcode461104
dc.subject.fieldofresearchcode4613
dc.titleRobust Multi-Objective Optimization using Conditional Pareto Optimal Dominance
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationMirjalili, SZ; Chalup, S; Mirjalili, S; Noman, N, Robust Multi-Objective Optimization using Conditional Pareto Optimal Dominance, 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, 2020
dc.date.updated2021-01-18T01:26:14Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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