To improve the efficiency of particle swarm optimization, a random particle swarm optimization algorithm is proposed on the basis of analyzing the search process of quantum particle swarm optimization lgorithm. The proposed algorithm has only a parameter, and its search step length is controlled by a random variable value. In this model, the target position can be accurately tracked by the reasonable design of the control parameter. The experimental results of standard test function extreme optimization and clustering optimization show that the proposed algorithm is superior to the quantum particle swarm optimization and the common particle swarm optimization algorithm in optimization ability and optimization efficiency.