Abstract: Particle swarm optimization (PSO) is an evolutionary computation technique developed by Kennedy and Eberhart in 1995. The underlying motivation for the development of PSO algorithm is social behavior of animals such as bird flocking, fish schooling, and swarm theory. Now PSO has been proved to be very effective for some problems. However, like other stochastic algorithms, PSO also suffers from the premature convergence problem, especially in the large scale and complex problems. In order to improve the global convergent ability of the standard particle swarm optimization (SPSO), a new version of particle swarm optimization named by a two-order PSO model is developed. Firstly, a two-order PSO model is introduced, its convergence analysis is given, and at the same time its parameter choices are studied. Secondly, a PSO model with the stochastic inertia weight is educed from the evolutionary equations of the two-order PSO. Thirdly, the two-order oscillating PSO with an oscillating factor is provided to adjust the influence of the acceleration on the velocity, which can guarantee the two-order PSO to converge to the global optimization validly. Finally, the above-proposed models are used to some benchmark optimizations. The experimental results show the proposed models can overcome the premature problem validly, and outperform the standard PSO in the global search ability and convergent speed.