Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization

Abstract

Covering the whole set of Pareto-optimal solutions is a desired task of multiobjective optimization methods. Because in general it is not possible to determine this set, a restricted amount of solutions are typically delivered in the output to decision makers. We propose a method using multiobjective particle swarm optimization to cover the Pareto-optimal front. The method works in two phases. In phase 1 the goal is to obtain a good approximation of the Pareto-front. In a second run subswarms are generated to cover the Pareto-front. The method is evaluated using different test functions and compared with an existing covering method using a real world example in antenna design.