Scalability study of particle swarm optimizers in dynamic environments

Abstract

This study investigates the scalability of three particle swarm optimizers (PSO) on dynamic environments. The charged PSO (CPSO), quantum PSO (QPSO) and dynamic heterogeneous PSO (dHPSO) algorithms are evaluated on a number of DF1 and moving peaks benchmark (MPB) environments that differ with respect to the severity and frequency of change. It is shown that dHPSO scales better to high severity and high frequency DF1 environments. For MPB environments, similar scalability results are observed, with dHPSO obtaining the best average results over all test cases. The good performance of dHPSO is ascribed to its ability to explore and exploit the search space more efficiently than CPSO and QPSO. © 2012 Springer-Verlag.