In order to optimize the current Particle Swarm Optimization (PSO) algorithm, which is easy to fall into local optimum, slow convergence and another faults, this study proposes an improved inertia weight parameter method to optimize the algorithm. Combining the operation of mutation operator in Differential Evolution (DE) algorithm to improve the self-adaptation of the algorithm and limit the speed and search space of the algorithm to prevent particles from jumping out of the prescribed search space. Choose the corresponding test function and compare the improved algorithm with the other two algorithms by using Matlab software. The results show that the improved algorithm has a certain improvement in the convergence speed and the stability of fitness value.