Heterogeneous Strategy Particle Swarm Optimization
This work addresses optimization challenges in domains like filter design, but it is incremental as it builds on existing PSO methods.
The authors tackled the problem of balancing exploration and exploitation in Particle Swarm Optimization (PSO) by proposing a heterogeneous strategy (HSPSO) that mixes fully informed and singly informed particles, resulting in improved performance over standard PSO and fully informed PSO in numerical experiments.
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.