A Hybrid Particle Swarm Algorithm for Function Optimization

Particle swarm optimization (PSO) is one of the evolutionary techniques based on swarm intelligence, which has show good performance in many optimization problems. This paper proposes a new learning strategy to help particles learn experiences from other previous best particles. In order to verify the proposed approach (HPSO), this paper investigates the effects of learning factor on six well-known benchmark functions. Additionally, comparison of HPSO with standard PSO and comprehensive learning PSO shows that HPSO outperforms them on most test functions.