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dc.contributor.authorClark, Mitchell
dc.date.accessioned2021-03-30T19:18:38Z
dc.date.available2021-03-30T19:18:38Z
dc.identifier.urihttp://hdl.handle.net/10464/15031
dc.description.abstractThe vast majority of real-world optimization problems can be put into the class of large-scale global optimization (LSOP). Over the past few years, an abundance of cooperative coevolutionary (CC) algorithms has been proposed to combat the challenges of LSOP’s. When CC algorithms attempt to address large scale problems, the effects of interconnected variables, known as variable dependencies, causes extreme performance degradation. Literature has extensively reviewed approaches to decomposing problems with variable dependencies connected during optimization, many times with a wide range of base optimizers used. In this thesis, we use the cooperative particle swarm optimization (CPSO) algorithm as the base optimizer and perform an extensive scalability study with a range of decomposition methods to determine ideal divide-and-conquer approaches when using a CPSO. Experimental results demonstrate that a variety of dynamic regrouping of variables, seen in the merging CPSO (MCPSO) and decomposition CPSO (DCPSO), as well varying total fitness evaluations per dimension, resulted in high-quality solutions when compared to six state-of-the-art decomposition approaches.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectAIen_US
dc.subjectLarge Scale Global Optimizationen_US
dc.subjectCooperative PSOen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectComparative Studyen_US
dc.titleComparative Study On Cooperative Particle Swarm Optimization Decomposition Methods for Large-scale Optimizationen_US
dc.typeElectronic Thesis or Dissertationen
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-02-12T00:00:00Z


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