Abstract: This paper presents a hybrid particle swarm optimization for solving constrained optimization problems, which is based on constraint-handling mechanism, SA (Simulated Annealing) and dynamically changing penalty function. And can continuously find better feasible solutions, gradually leading the search near the true optimum solution. This constraint handling approach have been tested on some problems commonly used in the literature. In all cases, our results show that the proposed approach is an efficient and can reach a higher precision, the precision is more than 10^-15.