Effectively multi-swarm sharing management for differential evolution

This paper presents a novel multi-swarm sharing management for differential evolution (MsSDE) to deal with numerical optimization effectively. Multi-swarm is an effective search concept to keep the original search characteristic or effective balance strategies. However, it still has some defects need to overcome, such as weak search ability for smaller swarm and easy to fall into local optimal position. In order to overcome the problem mention above, the proposed multi-swarm sharing management can adjust each swarm size, share and analyze their information for other swarms to get more effective search ability. Testing and comparing results with original DE and EPUS-PSO by several benchmark functions, it showed that the proposed method has satisfying performance.