Abstract: Aiming at the stagnation problem of the cooperative particle swarm optimization, this paper presents an improved cooperative particle swarm optimization. This proposed method adopts the cooperation principle of optimization algorithm, so it not only ensures the convergence rate, but also avoids plunging into local optimum. Moreover, both comprehensive learning and disturbing mechanism are introduced to strengthen the diversity of population and avoid the stagnation and plunging into local optimum. The new algorithm is tested by three typical functions and the flow shop scheduling problems, respectively. The simulation results show that the proposed algorithm can avoid the stagnation, improve the global convergence ability, and attain better optimization performance.