Multi-guide particle swarm optimization for many-objective optimization problems

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dc.contributor.advisorEngelbrecht, A. P.en_ZA
dc.contributor.authorSteenkamp, Cianen_ZA
dc.contributor.otherStellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Computer Science.en_ZA
dc.date.accessioned2021-06-07T13:55:23Z
dc.date.available2021-06-07T13:55:23Z
dc.date.issued2021-03
dc.descriptionThesis (MSc)--Stellenbosch University, 2021.en_ZA
dc.description.abstractENGLISH ABSTRACT: The scalability of the multi-guide particle swarm optimization (MGPSO) algorithm, with respect to the number of objectives for a problem, is investigated. Two MGPSO algorithm adaptations are proposed; that is, the partialdominance multi-guide particle swarm optimization (PMGPSO) algorithm and the knee-point driven multi-guide particle swarm optimization (KnMGPSO) algorithm. As a sub-objective, the effect of different archive balance coefficient update strategies for the MGPSO, the PMGPSO, and the KnMGPSO algorithms are investigated. The proposed algorithms attempt to address the scalability limitations associated with a certain component of the MGPSO algorithm. This study does not consider scalability with respect to the number of decision variables. This study assumes a static search space; that is, where the number of objectives remains fixed throughout the optimization. This study also assumes that each objective remains static throughout the search process. This study also considers only problems with boundary constraints. The results indicate that the MGPSO algorithm scaled to many-objectives competitively compared to other state-of-the-art many-objective optimization algorithms. The results were unexpected because the MGPSO algorithm uses the Pareto-dominance relation, which is known to degrade as the number of objectives increases. The proposed PMGPSO and KnMGPSO algorithms also scaled competitively, however, these algorithms were not superior to the MGPSO algorithm. The investigated dynamic archive balance coefficient update strategies did not improve the performance of the MGPSO, the PMGPSO, or the KnMGPSO algorithms. Keywords: Multi-guide particle swarm optimization, particle swarm optimization, many-objective optimization, scalabilityen_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Geen Afrikaanse opsomming beskikbaar.af_ZA
dc.description.versionMastersen_ZA
dc.format.extentxv, 409 pagesen_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/110568
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectOptimization problemsen_ZA
dc.subjectSwarm intelligenceen_ZA
dc.subjectOptimization algorithmsen_ZA
dc.subjectMathematical optimizationen_ZA
dc.subjectComputer networks -- Scalabilityen_ZA
dc.subjectUCTD
dc.titleMulti-guide particle swarm optimization for many-objective optimization problemsen_ZA
dc.typeThesisen_ZA
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