Aiming at the problem that the particle swarm optimization(PSO) algorithm trends to trap in local extreme, and performs high dimensional complex functions inefficiently, from perspectives of the cognitive analysis process of the system, a PSO algorithm based on Nolan model is proposed. The Tent chaotic map is introduced to improve the ergodicity of the algorithm, and the Euclidean distance index is given based on particle average position to automatically adjust particles’ position and ensure diversity to improve the global search capacity of the algorithm. Finally, typical functions are used to test the proposed method. Compared with the current algorithms, it is showed that the proposed method has the advantages in global search, efficiency and stability.