关键词: 粒子群算法, 中心位置, 学习因子, 收敛速度, 稳定性

Abstract: A modified Particle Swarm Optimization (PSO) on the basis of the two latest generations was proposed to solve the problem that no satisfactory results can be reached during later period of PSO, especially in high-dimensional search space. A new part was added to the velocity of replacement formula，suggesting that the particle comprehensively utilized the information from the previous two acts to instruct its next step. Primarily based on the record of recent changes of the current particle in the two latest generations, the central location of the previous two generations of the particle was calculated，the role of which was to point out the current global optimal position. The paper，at the same time, discussed deeply a new learning factor and their impact on the new modified algorithm. The experimental simulation results show that global searching ability, convergence rate, accuracy and stability of the new algorithm have been improved significantly.

Key words: Particle Swarm Optimization (PSO), central location, learning factor, convergence rate, stability