Abstract: The particle swarm optimization (PSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. The Lévy particle swarm optimization (Lévy PSO) was proposed. In the particle position updating formula, Lévy PSO eliminated the impact of speed on the convergence rate, and used Levy flight to change the direction of particle positions movement to prevent particles getting into local optimum value, and then using greedy strategy to update the evaluation and choose the best solution to obtain the global optimum. The experimental results show that Lévy PSO can effectively improve the accuracy and convergence speed and the Lévy PSO has better optimization effect than PSO, Cuckoo Search (CS) and Artificial Bee Colony Algorithm (ABC).

Key words: particle swarm optimization, Lévy fights;, greedy strategy, optimization