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dc.contributor.authorMadani, Amirali
dc.date.accessioned2021-09-03T17:13:01Z
dc.date.available2021-09-03T17:13:01Z
dc.identifier.urihttp://hdl.handle.net/10464/15142
dc.description.abstractMulti-guide particle swarm optimization (MGPSO) is a novel metaheuristic for multi-objective optimization based on particle swarm optimization (PSO). MGPSO has been shown to be competitive when compared with other state-of-the-art multi-objective optimization algorithms for low-dimensional problems. However, to the best of the author’s knowledge, the suitability of MGPSO for high-dimensional multi-objective optimization problems has not been studied. One goal of this thesis is to provide a scalability study of MGPSO in order to evaluate its efficacy for high-dimensional multi-objective optimization problems. It is observed that while MGPSO has comparable performance to state-of-the-art multi-objective optimization algorithms, it experiences a performance drop with the increase in the problem dimensionality. Therefore, a main contribution of this work is a new scalable MGPSO-based algorithm, termed cooperative co-evolutionary multi-guide particle swarm optimization (CCMGPSO), that incorporates ideas from cooperative PSOs. A detailed empirical study on well-known benchmark problems comparing the proposed improved approach with various state-of-the-art multi-objective optimization algorithms is done. Results show that the proposed CCMGPSO is highly competitive for high-dimensional problems.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectLarge-scale optimizationen_US
dc.subjectParticle Swarm optimizationen_US
dc.titleMulti-Guide Particle Swarm Optimization for Large-Scale Multi-Objective Optimization Problemsen_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.dateFOA2021-09-01T00:00:00Z


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