A Novel Hybrid Particle Swarm Optimizer: Tradeoff between Exploration and Exploitation

摘要

Hybridization is a useful method to enhance the performance of particle swarm optimizer (PSO). In this paper, a novel particle swarm optimizer (NHPSO) combining PSO with a constriction factor (CF-PSO) and the fully informed particle swarm optimizer (FIPSO) in cycles is proposed, in order to balance the convergence speed and search accuracy. Six most commonly used benchmarks are used to evaluate the strategy on the performance of PSOs. The results suggest NHPSO has a generally good performance in numerical optimization.