Fully learned multi-swarm particle swarm optimization

Abstract

This paper presents a new variant of PSO, called fully learned multiswarm particle swarm optimization (FLMPSO) for global optimization. In FLMPSO, the whole population is divided into a number of sub-swarms, in which the learning probability is employed to influence the exemplar of each individual and the center position of the best experience found so far by all the sub-swarms is also used to balance exploration and exploitation. Each particle updates its velocity based on its own historical experience or others relying on the learning probability, and the center position is also applied to adjust its flying. The experimental study on a set of six test functions demonstrates that FLMPSO outperform the others in terms of the convergence efficiency and the accuracy.