Inspired by the ideas of multi-swarm information sharing and elitist perturbation guiding a novel multi-swarm cooperative multistage perturbation guiding particle swarm optimizer (MCpPSO) is proposed in this paper. The multi-swarm information sharing idea is to harmoniously improve the evolving efficiency via information communicating and sharing among different sub-swarms with different evolution mechanisms. It is possible to drive a stagnated sub-swarm to revitalize once again with the beneficial information obtained from other sub-swarms. Multistage elitist perturbation guiding strategy aims to slow down the learning speed and intensity in a certain extent from the global best individual while keeping the elitist learning mechanism. It effectively enlarges the exploration domain and diversifies the flying tracks of particles. Extensive experiments indicate that the proposed strategies are necessary and cooperative, both of which construct a promising algorithm MCpPSO when comparing with other particle swarm optimizers and state-of-the-art algorithms. The ideas of central position perturbation along the global best particle, different computing approaches for central position and important parameters influence analysis are presented and analyzed. 
A multi-swarm cooperative multistage perturbation guiding particle swarm optimizer