A Dynamic Multi-Swarm Particle Swarm Optimization With Global Detection Mechanism

Abstract

To overcome the shortcomings of the standard particle swarm optimization algorithm (PSO), such as premature convergence and low precision, a dynamic multi-swarm PSO with global detection mechanism (DMS-PSO-GD) is proposed. In DMS-PSO-GD, the whole population is divided into two kinds of sub-swarms: several same-sized dynamic sub-swarms and a global sub-swarm. The dynamic sub-swarms achieve information interaction and sharing among themselves through the randomly regrouping strategy. The global sub-swarm evolves independently and learns from the optimal individuals of the dynamic sub-swarm with dominant characteristics. During the evolution process of the population, the variances and average fitness values of dynamic sub-swarms are used for measuring the distribution of the particles, by which the dominant one and the optimal individual can be detected easily. The comparison results among DMS-PSO-GD and other 5 well-known algorithms suggest that it demonstrates superior performance for solving different types of functions.