An Empirical Comparison of Parallel and Distributed Particle Swarm Optimization Methods

The goal of this paper is to present four new parallel and distributed particle swarm optimization methods and to experimentally compare their performances. These methods include a genetic algorithm whose individuals are co-evolving swarms, a different multiswarm system and their respective variants enriched by adding a repulsive component to the particles. We have tried to carry out this comparison using the benchmark test suite that has been defined for the CEC-2005 numerical optimization competition and we have remarked that it is hard to have a clear picture of the experimental results on that benchmark suite. We believe that this is due to the fact that the CEC-2005 benchmark suite is only composed by either very easy or very hard test functions. For this reason, we introduce two new sets of test functions whose difficulty can be tuned by simply modifying the values of few real-valued parameters. We propose to integrate the CEC-2005 benchmark suite by adding these sets of test functions to it. Experimental results on these two sets of test functions clearly show that the proposed repulsive multi-swarm system outperforms all the other presented methods.