Heterogeneous Strategy Particle Swarm Optimization

TL;DR

This paper introduces a heterogeneous strategy particle swarm optimization (HSPSO) that combines different particle behaviors to improve convergence speed and solution quality, outperforming traditional PSO methods.

Contribution

The paper proposes a novel HSPSO algorithm that integrates fully informed and singly informed particles, enhancing performance and diversity in optimization tasks.

Findings

HSPSO outperforms standard PSO and fully informed PSO in experiments.

The cooperation between particle types balances exploration and exploitation.

HSPSO is effective in filter design problems.

Abstract

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.