To improve the optimizing accuracy, and solve the problem of falling into local optima and the lower rate of convergence in cooperative particles swarm optimization, an improved cooperative particles swarm optimization algorithm is proposed. The proposed approach combines the strong global search ability of genetic algorithm and the excellent local search ability of extreme optimization algorithm. Firstly, an improved strategy is presented for particle swarm optimization. Then, the genetic algorithm is used to increase the diversity and optimal benign of the particles. After a certain iterations intervals, extreme optimization is adopted to accelerate the convergence. The experimental results show that the proposed approach can improve the optimal performance, escape from local optima, and enhance the rate of convergence.