Title: The Influence of Swarm Topologies in Many-Objective Optimization Problems
Abstract: Particle Swarm Optimization (PSO) is a bio-inspired metaheuristic that has been successfully adopted for single- and multi-objective optimization. Several studies show that the way in which particles are connected with each other (the swarm topology) influences PSO’s behavior. A few of these studies have focused on analyzing the influence of swarm topologies on the performance of Multi-objective Particle Swarm Optimizers (MOPSOs) using problems with two or three objectives. However, to the authors’ best knowledge such studies have not been done so far for many-objective optimization problems. This paper provides an analysis of the influence of the ring, star, lattice, wheel, and tree topologies on the performance of SMPSO (a well-known Pareto-based MOPSO) using many-objective problems. Based on these results, we also propose two MOPSOs that use a combination of topologies: SMPSO-SW and SMPSO-WS. Our experimental results show that SMPSO-SW is able to outperform SMPSO in most of the test problems adopted.
Publication Year: 2021
Publication Date: 2021-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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