A Particle Swarm Optimization Method for Multimodal Optimization Based on Electrostatic Interaction

The problem of finding more than one optimum of a fitness function has been addressed in evolutionary computation using a wide variety of algorithms, including particle swarm optimization (PSO). Several variants of the PSO algorithm have been developed to deal with this sort of problem with different degrees of success, but a common drawback of such approaches is that they normally add new parameters that need t o be properly tuned, and whose values usually rely on previous knowledge of the fitness function being analyzed. In this paper, we present a PSO algorithm based on electrostatic interaction, which does not need any additional parameters besides those of the original PSO. We show that our proposed approach is able to converge to all the optima of several test functions commonly adopted in the specialized literature, consuming less evaluations of the fitness function than other previously reported PSO methods.