A Multiple Objective Particle Swarm Optimization Approach Using Crowding Distance and Roulette Wheel

Abstract

This paper presents a multiobjective optimization algorithm based on Particle Swarm Optimization (MOPSO-CDR) that uses a diversity mechanism called crowding distance to select the social leaders and the cognitive leader. We also use the same mechanism to delete solutions of the external archive. The performance of our proposal was evaluated in five well known benchmark functions using four metrics previously presented in the literature. Our proposal was compared to other four multi objective optimization algorithms based on Particle Swarm Optimization, called m-DNPSO, CSS-MOPSO, MOPSO and MOPSO-CDLS. The results showed that the proposed approach is competitive when compared to the other approaches and outperforms the other algorithms in many cases.