Heterogeneous Strategy Particle Swarm Optimization

Particle swarm optimization (PSO) is a widely recognized optimization algorithm inspired by social swarm. In this brief, we present a heterogeneous strategy PSO (HSPSO), in which a proportion of particles adopts a fully informed strategy to enhance the converging speed while the rest is singly informed to maintain the diversity. Our extensive numerical experiments show that the HSPSO algorithm is able to obtain satisfactory solutions, outperforming both PSO and the fully informed PSO. The evolution process is examined from both structural and microscopic points of view. We find that the cooperation between two types of particles can facilitate a good balance between exploration and exploitation, yielding better performance. We demonstrate the applicability of HSPSO on the filter design problem.