Abstract: The paper presents a Particle Swarm Optimization algorithm based on crowding distance and Dynamic Weighted Aggregation（CDDWA-PSO） for multi-objective optimization problems.PSO is modified by storing nondominated solutions externally and selecting a nondominated solution from external archive randomly using as the global best.The proposed algorithm introduces the Pareto dominance relationship and crowding distance of fitness to preserve population diversity，and incorporates DWA procedure to close to the best solution of every objective.Several benchmark cases are tested and the results show that the method can efficiently find multiple Pareto optimal solutions well.