Particle swarm optimization for constrained multimodal function optimization

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dc.contributor.advisorEngelbrecht, Andriesen_ZA
dc.contributor.authorStrelitz, Benjamin Steenvelden_ZA
dc.contributor.otherStellenbosch University. Faculty of Science. Dept. of Computer Science.en_ZA
dc.date.accessioned2024-02-25T15:44:54Z
dc.date.accessioned2024-04-26T08:59:13Z
dc.date.available2024-02-25T15:44:54Z
dc.date.available2024-04-26T08:59:13Z
dc.date.issued2024-03
dc.descriptionThesis (MSc)--Stellenbosch University, 2024.en_ZA
dc.description.abstractENGLISH ABSTRACT: This thesis investigates the efficiency of particle swarm optimization (PSO) algorithms at finding many feasible global optima for constrained multimodal optimization prob- lems. The proposed approach is the niching migratory multi-swarm optimizer with Deb's comparison criteria (NMMSO-DCC) algorithm. The NMMSO-DCC algorithm uses the same core architecture as the niching migratory multi-swarm optimization (NMMSO) al- gorithm, but uses Deb's comparison criteria as a constraint handling method. Deb's com- parison criteria allows the NMMSO-DCC algorithm to find many feasible global optima for constrained multimodal optimization problems (CMMOPs), whereas the NMMSO algorithm was designed only to find global optima for boundary constrained multimodal optimization problems (MMOPs). The NMMSO algorithm is one of the state-of-the-art multiomodal optimization algorithms, but cannot be used when constraints are placed on the objective function. Thus, the proposed algorithm addresses the inability of the NMMSO algorithm to solve constrained multimodal optimization problems. This study assumes that the objective function to be optimized remains static throughout the search process. This study also assumes that the constraints placed upon the objective func- tion remain static during the search process. All benchmark problems in this study contain boundary constraints. The results indicate that the NMMSO-DCC performs competitively compared to other state-of-the-art constrained multimodal optimization algorithms. The results in terms of success rate are particularly convincing, whereas NMMSO-DCC struggled more with respect to the peak ratio. This means that although the NMMSO-DCC algorithm is able to locate all global optima within a given tolerance level in some of the independent runs, it struggles to do so consistently across multiple independent runs.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Die tesis ondersoek die doeltreffendheid van algoritrnes vir deeltjieswerrnoptirnering orn verskeie lewensvatbare globale optirnerings vir beperkte rnultirnodale optirneringsprob- lerne te vind. Die voorgestelde benadering is orn die rnigrerende rnulti-swerrnoptirnering rnet die algoritrne van Deb se vergelykingskriteria (NMMSO-DCC) te nis. Die NMMSO- DCC-algoritrne gebruik dieselfde kern-argitektuur as die algoritrne van die nis-rnigrerende rnulti-swerrnoptirnering (NMMSO), rnaar gebruik Deb se vergelykingskriteria as 'n beperk- ende hanteringsrnetode. Deb se vergelykingskriteria rnaak dit rnoontlik vir die NMMSO- DCC-algoritrne orn verskeie uitvoerbare globale optirna vir beperkte rnultirnodale opti- rneringsproblerne te vind, terwyl die NMMSO-algoritrne slegs ontwerp is orn globale op- tirna vir begrensende rnultirnodale optirneringsproblerne te vind. Die NMMSO-algoritrne is een van die rnees gevorderde rnultirnodale optirneringsalgoritrnes, rnaar kan nie ge- bruik word wanneer beperkings op die doelfunksie geplaas word nie. Dus spreek die voorgestelde algoritrne die onverrnoe van die NMMSO-algoritrne aan orn beperkte rnul- tirnodale optirneringsproblerne op te los. Die studie veronderstel dat die doelfunksie wat geoptirneer rnoet word, deur die soekproses staties bly. Die studie veronderstel ook dat die beperkings wat op die doelfunksie geplaas word, deurgaans tydens die soekproses staties bly. Alle toetsproblerne in hierdie studie bevat grensbeperkings. Die resultate dui daarop dat die NMMSO-DCC rnededingend presteer in vergelyking rnet ander gevorderde beperkte rnultirnodale optirneringsalgoritrnes. Die resultate se sukseskoers is besonder oortuigend, terwyl NMMSO-DCC rneer problerne ondervind het rnet die piekverhouding. Dit beteken dat alhoewel die NMMSO-DCC-algoritrne in sornrnige van die onafhanklike lopies al die globale optirna binne 'n gegewe toleransievlak kan opspoor, vind dit nie konsekwent oor veelvuldige onafhanklike lopies plaas nie.af_ZA
dc.description.versionMastersen_ZA
dc.identifier.urihttps://scholar.sun.ac.za/handle/10019.1/130201
dc.language.isoen_ZAen_ZA
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subject.lcshParticle swarm optimizationen_ZA
dc.subject.lcshSwarm intelligenceen_ZA
dc.subject.lcshAlgorithmsen_ZA
dc.subject.lcshConstrained optimization problems -- Mathematical modelsen_ZA
dc.subject.nameUCTDen_ZA
dc.titleParticle swarm optimization for constrained multimodal function optimizationen_ZA
dc.typeThesisen_ZA
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