Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance

A new Multi-Objective Particle Swarm Optimizer is proposed, which is based on Pareto dominance and the use of a crowding factor to filter out the list of available leaders and incorporates the ∈-dominance concept to fix the size of the set of final solutions produced by the algorithm.

Abstract

In this paper, we propose a new Multi-Objective Particle Swarm Optimizer, which is based on Pareto dominance and the use of a crowding factor to filter out the list of available leaders. We also propose the use of different mutation (or turbulence) operators which act on different subdivisions of the swarm. Finally, the proposed approach also incorporates the ∈-dominance concept to fix the size of the set of final solutions produced by the algorithm. Our approach is compared against five state-of-the-art algorithms, including three PSO-based approaches recently proposed. The results indicate that the proposed approach is highly competitive, being able to approximate the front even in cases where all the other PSO-based approaches fail.