Abstract: A hybrid particle swarm optimization (HPSO) is proposed, where the Hooke-Jeeves pattern search is combined with PSO to speed up the local search, also mutation operation is embedded to avoid the common defect of premature convergence. Two thresholds are adopted to balance the exploration and exploitation abilities. The perfor-mance of new algorithm is demonstrated through extensive benchmark functions and compared with that of the PSO. The obtained results show that the local search ability is improved, and the probability of finding the global optimal value by HPSO is larger than that by using PSO.