A COMPARATIVE STUDY OF NEIGHBORHOOD TOPOLOGIES FOR PARTICLE SWARM OPTIMIZERS
Angelina Jane Reyes-Medina, Gregorio Toscano‐Pulido, José Gabriel Ramírez-Torres · International Conference on Evolutionary Computation · 2018
CINVESTAV-Tamaulipas. Km. 6 carretera Cd. Victoria-Monterrey, Cd. Victoria, Tamaulipas, 87261, [email protected], [email protected], [email protected]: Particle swarm optimization, Neighborhood topologies, Parameter setting.Abstract: Particle swarm optimization (PSO) is a meta-heuristic that has been found to be very successful in a widevariety of optimization tasks. The behavior of any meta-heuristic for a given problem is directed by both: thevariation operators, and the values selected for the parameters of the algorithm. Therefore, it is only naturalto expect that not only the parameters, but also the neighborhood topology play a key role in the behaviorof PSO. In this paper, we want to analyze whether the type of communication employed to interconnect theswarm accelerates or affects the algorithm convergence. In order to perform a wide study, we selected sixdifferent neighborhoods topologies: ring, fully connected, mesh, toroid, tree and star; and two clusteringalgorithms: k-means and hierarchical. Such approaches were incorporated into three PSO versions: the basicPSO, the Bare-bones PSO (BBPSO) and an extension of BBPSO called BBPSO(EXP). Our results indicatethat 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-basedalgorithm.