Abstract: A novel particle swarm optimization algorithm was proposed, which was adaptive chaos particle swarm optimization algorithm based on swarm premature convergence degree and nonlinear periodic oscillating strategy. The ergodic of chaos was used for initializing the velocities and positions of the particles. The inertia weights were adjusted adaptively according to the swarm’s premature convergence degree and the particles’ fitnesses, and the nonlinear periodic oscillating strategy was used for the learning coefficients, which simulates the decentralization and regroup of the birds when they were foraging. The simulation results on benchmark functions show that the proposed algorithm not only has fast convergent rate and high quality of optimization, but also has good stability.