An Adaptive particle swarm optimization algorithm is proposed. Algorithm combines with pareto local search (PLS) method and adaptively adjusts flying time. During the search process, our algorithm can enhance the local search ability of particle swarm optimization (PSO) thought adding random perturbation to local search. The flying time of every particle in our algorithm can adaptively adjust with the evolutionary generations. Some optimization tests of the standard benchmark function confirm that our method has a stronger ability of global optimization.