Abstract: In allusion to particle swarm optimization algorithm (PSO) many defects such as being easy to trap into local optima, slow convergence in the end of evolution stage and low computational precision, proposes an improved adaptive evolutionary PSO algorithm.A pheromone diffusion function, which can control the degree of convergence of particles move to the best position, is designed by both taking into account these particles distribution and their fitness value.Adjusting inertial weight adaptively with diversity feedback control is built into the improved PSO, and makes use of mutation to greatly contribute to breaking away from local optima.Experiments on optimization of high-dimension benchmark functions show that the improved algorithm can find better optima with converges faster, and prevent more effectively the premature convergence.