一种改进的微粒群优化算法

Improved Particle Swarm Optimization Algorithm

标准微粒群优化(PSO)算法是一种群体智能算法,它容易陷入局部极值点,进化后期收敛速度慢且精度较差,而且参数的选择对算法的优劣影响很大。针对这些缺点,首先提出了一种在位置进化方程中引进动态参数的方法,改进了标准微粒群算法收敛速度;然后通过在速度、位置进化方程中同时引进动态参数来提高算法收敛速度和收敛率。经J．D．Schaffer函数和LevyNo．5函数对改进算法的测试表明,相比于标准微粒群算法,该方法的收敛速度和平均收敛率均有大幅度提高。

The normal partical swarm optimization （PSO） algorithm is a kind of swarm intelligence methods. It is easy to trapped into local extremum,and its convergence speed is lower and the precision is worse in the late evolution. Furthermore, the parameter selection can affect the algorithm. Aimed at these disadvantages of normal PSO, the new algorithm by introducing dynamical parameters in the evolution of the position equation is proposed first. The convergence speed is improved in the new algorithm. And then, by introducing dynamical parameters in the evolution of the speed equation and the position equation at the same time, the new algorithm improves its convergence speed and convergence rate. The new method tested by functions J. D. Schaffer and Levy No. 5 shows that the convergence speed and the average convergence rate increase.