To solve the problem of premature convergence in traditional particle swarm optimization (PSO) and differential evolution (DE), a hybrid algorithm based on particle swarm optimization with stochastic differential mutation is proposed in this paper. Combining with the characteristics between PSO and DE, the new algorithm firstly generates a candidate individual using differential mutation, and then put the individual into velocity update formula leading flight direction of particle, which can expand the search space and enhance the global explorative ability of algorithm. Meanwhile, a stochastic differential mutation method is presented to disturb the current optimal particle in order to avoid the best particle being trapped into local optima, since which may cause search stagnation. The new algorithm compared with three related algorithms on 8 benchmark functions including unimodal and multimodal test functions. The experimental results show that the new hybrid algorithm outperforms other comparative algorithms and greatly improves performance of algorithm.