In order to solve the problems of premature convergence and local optimization in Particle Swarm Optimization (PSO), a strategy for non-linear improvement of inertia weight is proposed, an inertia weight based on exponential function is constructed, and a random adjustment strategy is added to realize the dynamic adjustment of inertia weight.In addition, mutation and crossover operations in differential evolution algorithm are introduced to update the position of particles in order to increase the diversity of particle population.To verify the optimization performance of the proposed algorithm, four typical test functions are selected to compare the improved PSO algorithm with other algorithms.The experimental data shows that the improved algorithm proposed in this paper has higher search accuracy for complex problems and faster convergence speed for simple problems.