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dc.contributor.authorCrane, Tyler
dc.date.accessioned2021-03-30T19:22:17Z
dc.date.available2021-03-30T19:22:17Z
dc.identifier.urihttp://hdl.handle.net/10464/15032
dc.description.abstractMultimodal optimization (MMO) techniques have been researched and developed over the years to track multiple global optima concurrently. MMO algorithms extend traditional unimodal optimization algorithms by using search strategies built around forming niches for multiple possible solutions. NichePSO was one of the first approaches to utilize particle swarm optimization (PSO) for MMO problems, using several small subswarms of agents working concurrently to form niches within the search space. Despite its promising performance NichePSO does suffer from some problems, and very little research has been done to study and improve upon the algorithm over the years. A main goal of this thesis is to analyze the NichePSO algorithm, gaining insight into the strengths and weaknesses of the algorithm. Empirical analyses were performed to study the NichePSO’s ability to maintain niches within complex problem domains, as well as methods for improving the overall performance and effectiveness of the algorithm. Two variants of the NichePSO algorithm are proposed, and experimental results show that they both significantly improve the performance of the NichePSO algorithm across several benchmark functions.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectmultimodalen_US
dc.subjectnichingen_US
dc.subjectPSOen_US
dc.subjectanalysisen_US
dc.titleAnalysis of the Niching Particle Swarm Optimization Algorithmen_US
dc.typeElectronic Thesis or Dissertationen_US
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.dateFOA2021-08-15T02:13:09Z


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