A Study of Parallel and Distributed Particle Swarm Optimization

The goal of this paper is to present four new parallel and distributed particle swarm optimization methods and to experimentally compare their performances on a wide set of benchmark functions. These methods include a genetic algorithm whose individuals are co-evolving swarms, an "island model"-based multi-swarm system, where swarms are independent and they interact by means of particle migrations at regular time steps, and their respective variants enriched by adding a repulsive component to the particles. The benchmark functions used in our experimental study are two new sets of test functions (whose difficulty can be tuned by simply modifying the values of few real-valued parameters), the well known Rastrigin test functions, and the test functions proposed in the CEC 2005 numerical optimization competition. We show that the proposed repulsive multi-swarm system outperforms all the other presented methods.