A novel diversity guided Particle Swarm multi-objective Optimization algorithm

This paper presents a multi-objective diversity guided Particle Swarm Optimization approach named MOPSO-AR which increases diversity performance of multi-objective Particle Swarm optimization by using Attraction and Repulsion (AR) mechanism. AR mechanism uses a diversity measure to control the swarm. Being attractive and repulsive will help to overcome the problem of premature convergence. AR mechanism together with crowding distance computation and mutation operator maintains the diversity of non-dominated set in external archive. The approach is verified by several test function experiments. Results demonstrate that the proposed approach is highly competitive in distribution of non-dominated solutions but still keeps convergence towards the Pareto front.