  PSO is a widely recognized optimization algorithm inspired by social swarm.
In this brief we present a heterogeneous strategy particle swarm optimization
(HSPSO), in which a proportion of particles adopt a fully informed strategy to
enhance the converging speed while the rest are singly informed to maintain the
diversity. Our extensive numerical experiments show that 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.
