Dynamic multi-swarm particle swarm optimizer with cooperative learning strategy

Abstract

In this article, the dynamic multi-swarm particle swarm optimizer (DMS-PSO) and a new cooperative learning strategy (CLS) are hybridized to obtain DMS-PSO-CLS. DMS-PSO is a recently developed multi-swarm optimization algorithm and has strong exploration ability for the use of a novel randomly regrouping schedule. However, the frequently regrouping operation of DMS-PSO results in the deficiency of the exploitation ability. In order to achieve a good balance between the exploration and exploitation abilities, the cooperative learning strategy is hybridized to DMS-PSO, which makes information be used more effectively to generate better quality solutions. In the proposed strategy, for each sub-swarm, each dimension of the two worst particles learns from the better particle of two randomly selected sub-swarms using tournament selection strategy, so that particles can have more excellent exemplars to learn and can find the global optimum more easily. Experiments are conducted on some well-known benchmarks and the results show that DMS-PSO-CLS has a superior performance in comparison with DMS-PSO and several other popular PSO variants.