Multi-guide particle swarm optimization for many-objective optimization problems
| dc.contributor.advisor | Engelbrecht, A. P. | en_ZA |
| dc.contributor.author | Steenkamp, Cian | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Computer Science. | en_ZA |
| dc.date.accessioned | 2021-06-07T13:55:23Z | |
| dc.date.available | 2021-06-07T13:55:23Z | |
| dc.date.issued | 2021-03 | |
| dc.description | Thesis (MSc)--Stellenbosch University, 2021. | en_ZA |
| dc.description.abstract | ENGLISH ABSTRACT: The scalability of the multi-guide particle swarm optimization (MGPSO) algorithm, with respect to the number of objectives for a problem, is investigated. Two MGPSO algorithm adaptations are proposed; that is, the partialdominance multi-guide particle swarm optimization (PMGPSO) algorithm and the knee-point driven multi-guide particle swarm optimization (KnMGPSO) algorithm. As a sub-objective, the effect of different archive balance coefficient update strategies for the MGPSO, the PMGPSO, and the KnMGPSO algorithms are investigated. The proposed algorithms attempt to address the scalability limitations associated with a certain component of the MGPSO algorithm. This study does not consider scalability with respect to the number of decision variables. This study assumes a static search space; that is, where the number of objectives remains fixed throughout the optimization. This study also assumes that each objective remains static throughout the search process. This study also considers only problems with boundary constraints. The results indicate that the MGPSO algorithm scaled to many-objectives competitively compared to other state-of-the-art many-objective optimization algorithms. The results were unexpected because the MGPSO algorithm uses the Pareto-dominance relation, which is known to degrade as the number of objectives increases. The proposed PMGPSO and KnMGPSO algorithms also scaled competitively, however, these algorithms were not superior to the MGPSO algorithm. The investigated dynamic archive balance coefficient update strategies did not improve the performance of the MGPSO, the PMGPSO, or the KnMGPSO algorithms. Keywords: Multi-guide particle swarm optimization, particle swarm optimization, many-objective optimization, scalability | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING: Geen Afrikaanse opsomming beskikbaar. | af_ZA |
| dc.description.version | Masters | en_ZA |
| dc.format.extent | xv, 409 pages | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/110568 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Optimization problems | en_ZA |
| dc.subject | Swarm intelligence | en_ZA |
| dc.subject | Optimization algorithms | en_ZA |
| dc.subject | Mathematical optimization | en_ZA |
| dc.subject | Computer networks -- Scalability | en_ZA |
| dc.subject | UCTD | |
| dc.title | Multi-guide particle swarm optimization for many-objective optimization problems | en_ZA |
| dc.type | Thesis | en_ZA |