A hybrid particle swarm optimizer

Particle Swarm Optimization (PSO) is a recently proposed population-based evolutionary algorithm, which shows good search abilities in many optimization problems. However, PSO easily suffers from premature convergence when solving multimodal problems. In this paper, a hybrid PSO algorithm, called HPSO, is proposed by employing an improved crossover operator to deal with multimodal problems. In order to verify the performance of the proposed approach, six well-known multimodal benchmark problems were selected into our experiments. The simulation results show that the proposed approach HPSO outperforms standard PSO and classical evolutionary programming (CEP) in all test cases.