基于混沌的弹性粒子群全局优化算法

Resilient particle swarm global optimization algorithm based on chaos

为了克服粒子群优化容易陷入局部极小的缺陷,利用粒子速度不依赖于其与最优粒子之间距离的大小,而仅依赖其方向信息的特点,采用自适应策略弹性地修正粒子速度的幅值.同时,充分利用混沌运动的遍历性、随机性及对初值的敏感性等特性,提出一种基于混沌的弹性粒子群优化(CRPSO)算法,并将其成功用于典型多极点函数优化.仿真结果表明,该算法增强了摆脱局部极值点的能力,提高了收敛速度和精度.

To overcome the vice that the particle swarm optimization is prone to trap into local minima, by using a strategy in which the velocity 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. At the same time, by making the best of the ergodicity, stochastic property and regularity of chaos, a resilient particle swarm global optimization algorithm based on chaos is proposed, which is applied to optimize the functions having many apices. Simulation results show that the new algorithm has the ability to avoid being trapped in local minima, and improves computational precision and convergence speed.