Multi-Guide Particle Swarm Optimization for Large-Scale Multi-Objective Optimization Problems
dc.contributor.author | Madani, Amirali | |
dc.date.accessioned | 2021-09-03T17:13:01Z | |
dc.date.available | 2021-09-03T17:13:01Z | |
dc.identifier.uri | http://hdl.handle.net/10464/15142 | |
dc.description.abstract | Multi-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.iso | eng | en_US |
dc.publisher | Brock University | en_US |
dc.subject | Multi-objective optimization | en_US |
dc.subject | Large-scale optimization | en_US |
dc.subject | Particle Swarm optimization | en_US |
dc.title | Multi-Guide Particle Swarm Optimization for Large-Scale Multi-Objective Optimization Problems | en_US |
dc.type | Electronic Thesis or Dissertation | en |
dc.degree.name | M.Sc. Computer Science | en_US |
dc.degree.level | Masters | en_US |
dc.contributor.department | Department of Computer Science | en_US |
dc.degree.discipline | Faculty of Mathematics and Science | en_US |
refterms.dateFOA | 2021-09-01T00:00:00Z |