Concerning the problem that Particle Swarm Optimization (PSO) falls into local minima easily and converges slowly at the last stage, a kind of hybrid PSO algorithm with cooperation of multiple particle roles (MPRPSO) was proposed. The concept of particle roles was introduced into the algorithm to divide the population into three roles: Exploring Particle (EP), Patrolling Particle (PP) and Local Exploiting Particle (LEP). In each iteration, EP was used to search the solution space by the standard PSO algorithm, and then PP which was based on chaos was used to strengthen the global search capability and replace some EPs to restore population vitality when the algorithm trapped in local optimum. Finally, LEP was used to strengthen the local search to accelerate convergence by unidimensional asynchronous neighborhood search. The 30 times independent runs in the experiment show that, the proposed algorithm in the conditions that particle roles ratio is 0.8∶〖KG-*3〗0.1∶〖KG-*3〗0.1 has the mean value of 2.352E-72,4.678E-29,7.780E-14 and 2.909E-14 respectively in Sphere, Rosenbrock, Ackley and Quadric, and can converge to the optimal solution of 0 in Rastrigrin and Griewank, which is better than the other contrastive algorithms. The experimental results show that proposed algorithm improves the optimal performance with certain robustness.