Aimed at convergence and convergence rate of the standard particle swarm algorithm, analyzing inertial parameters of the standard particle swarm algorithm affects the performance optimization, an adaptive change in inertia weight of particle swarm optimization algorithm (ACPSO) is proposed. By analyzing the particle velocity and the process of the changing position, combined with the degree of premature convergence and individual adaptive value adjust the inertia weight, the algorithm has a good balance between the global convergence and convergence rate. And a typical function test shows that this method is effective in controlling the particle swarm diversity, and it also has good convergence rate.