Collaborative diversity control strategy for random drift particle swarm optimization

Abstract

Random drift particle swarm optimization (RDPSO) is a swarm intelligence algorithm inspired by the trajectory analysis of the canonical particle swarm optimization (PSO) and the free electron model in metal conductors placed in an external electric field. However, the RDPSO algorithm may easily encounter premature convergence when solving multimodal optimization problems. In order to deal with this issue, a new collaborative diversity control strategy for RDPSO is presented in this paper. Within this strategy, two kinds of diversity measures are used and changed in a collaborative manner to make the evolving process of the RDPSO controllable, so that premature convergence can be avoided and a final good solution can be found. Experimental results, when comparing with the canonical RDPSO and the canonical RDPSO using ring neighborhood topology, show that the proposed collaborative diversity control strategy can significantly improve the performance of the RDPSO algorithm for multimodal optimization problems in most cases.