关键词: 粒子群算法, 优化, 种群多样性

Abstract: To overcome premature searching by standard Particle Swarm Optimization（PSO） algorithm，a new modified PSO with information of the closest particle is proposed.In the algorithm，the particle is updated not only by the best previous position and the best position among all the particles in the swarm，but also by the best previous position of the closest particle.To balance the trade-off between exploration and exploitation and convergence to the global optimum solution，a linearly varying acceleration coefficient over the generations is introduced.The simulation results show that the algorithm has better probability of finding global optimum and mean best value than others algorithm，especially for multimodal function.

Key words: particle swarm optimization, optimization, population diversity