An improving multi-objective particle swarm optimization

Abstract

In the past few years, a number of researchers have successfully extended particle swarm optimization to multiple objectives. However, it still is an important issue to obtain a well-converged and well-distributed set of Pareto-optimal solutions. In this paper, we propose a fuzzy particle swarm optimization algorithm based on fuzzy clustering method and fuzzy strategy and archive update. The particles are evaluated and the dominated solutions are stored into different cluster in the generation, while dominated solutions are pruned. The non-dominated solutions are selected by fuzzy strategy, and the non-dominated solutions are added to the archive. It is observed that the proposed fuzzy particle swarm optimization algorithm is a competitive method in the terms of convergence near to the Pareto-optimal front, diversity of solutions. © 2010 Springer-Verlag Berlin Heidelberg.