A Comparative Study of Neighborhood Topologies for Particle Swarm Optimizers

Particle swarm optimization (PSO) is a meta-heuristic that has been found to be very successful in a wide variety of optimization tasks. The behavior of any meta-heuristic for a given problem is directed by both: the variation operators, and the values selected for the parameters of the algorithm. Therefore, it is only natural to expect that not only the parameters, but also the neighborhood topology play a key role in the behavior of PSO. In this paper, we want to analyze whether the type of communication employed to interconnect the swarm accelerates or affects the algorithm convergence. In order to perform a wide study, we selected six different neighborhoods topologies: ring, fully connected, mesh, toroid, tree and star; and two clustering algorithms: k-means and hierarchical. Such approaches were incorporated into three PSO versions: the basic PSO, the Bare-bones PSO (BBPSO) and an extension of BBPSO called BBPSO(EXP). Our results indicate that the convergence rate of a PSO-based approach has an strongly dependence of the topology used. However, we also found that the topology most widely used is not necessarily the best topology for every PSO-based algorithm.