【摘要】 当某个粒子与最优粒子很接近时,其飞行速度将趋于零,这是粒子群优化算法容易陷入局部极小的主要原因.为此,提出一种弹性粒子群优化算法.算法中,粒子速度不依赖其与最优粒子之间距离的大小,而仅依赖于其方向信息,并采用一种自适应策略弹性地修正粒子速度的幅值.将弹性粒子群优化算法应用于几种典型测试函数的优化,数值仿真结果表明,弹性粒子群优化算法能有效地找出全局最优点.更多还原

【Abstract】 When an individual is closed to the optimal particle,its velocity will approximate to zero.This is the main reason why particle swarm optimization(PSO) algorithm is prone to trap into local minima.A resilient particle swarm optimization(RPSO) is proposed,in which the velocity of an individual is not dependent on the size of distance between the individual and the optimal particle but only dependent on its direction.An adaptive scheme is adopted to adjust the magnitude of the velocity resiliently.Finally,RPSO is applied to optimize several test functions.Simulation results show that RPSO can find global optima effectively.更多还原