中文摘要：

为了进一步提高粒子群优化（PSO）算法的性能，分析了PSO算法的信息共享机制及由个体最优位置构成的平衡点的作用，探讨了一个好的平衡点应满足的条件。在此分析基础上，根据对粒子邻域个体最优位置的不同利用方式，提出了两种利用有效信息的PSO（EIPSO）算法形式：EIPSO-1与EIPSO-2．EIPS01算法中粒子的平衡点由性能不差于粒子当前位置的邻域个体最优位置组成，EIPSO-2中粒子的平衡点由粒子群中性能不差于当前粒子个体最优位置的粒子个体最优位置组成．EIPSO既充分利用了优秀邻域个体的信息，又避免了较差邻域个体的负面影响．5个测试函数的仿真结果及与其他PSO算法的比较结果验证了新算法的有效性．

英文摘要：

To further improve the performance of the particle swarm optimization （PSO） algorithm, sharing mechanism and the role of the equilibrium point in the PSO algorithm were investigated. Th the information e conditions for a good equilibrium point were also discussed. Based on the above analysis, two kinds of effectively informed PSO （EIPSO） algorithms, EIPSO-1 and EIPSO-2, were proposed. In the EIPSO-1 algorithm, the particle~ equilibrium is composed of the optimal position of individuals in its neighboring region whose performance is better than or equal to its current position. In the EIPSO-2 algorithm, the particle＇s equilibrium point is composed of the optirdal position of individual particles whose performance is equal to or better than that of current individual particles. Thus the particle can not only make full use of information of best neighboring individuals, but also avoid the negative influence of neighbors with bad performance. The efficiency of the new algorithm was verified by simulation with 5 benchmark functions, by comparing its results with those from other PSO algorithms.