A Generic Archive Technique for Enhancing the Niching Performance of Evolutionary Computation

Abstract

The performance of a multimodal evolutionary algorithm is highly sensitive to the setting of population size. This paper introduces a generic archive technique to reduce the importance of properly setting the population size parameter. The proposed archive technique contains two components: subpopulation identification and convergence detection. The first component is used to identify subpopulations in a number of individuals while the second one is used to determine whether a subpopulation is converged. By using the two components, converged subpopulations are identified, and then, individuals in the converged subpopulations are stored in an external archive and re-initialized to search for other optima. We integrate the archive technique with several state-of-the-art PSO-based multimodal algorithms. Experiments are carried out on a recently proposed multimodal problem set to investigate the effect of the archive technique. The experimental results show that the proposed method can reduce the influence of the population size parameter and improve the performance of multimodal algorithms.