改进惯性权重的简化粒子群优化算法

Simplified Particle Swarm Optimization Algorithm Based on Improved Inertia Weight

针对传统的粒子群优化算法收敛速度慢、易陷入局部空间极值的缺点,提出一种基于简化粒子群优化算法同时改进惯性权重的新算法.该算法首先去掉速度项,使算法更加简便,然后改进位移项,最后改进惯性权重.对6个经典函数分别采用传统的粒子群优化算法、简化的粒子群优化算法和该改进的算法进行比较,数值实验表明,该改进的粒子群优化算法比其他两个算法的性能好.

For the shortcomings of the traditional particle swarm optimization algorithm,which is easy to fall into local extreme,a new algorithm based on the simplified particle swarm optimization algorithm is proposed.Firstly,it removes the speed term,so it makes the algorithm simple.And then it mproves the dis-placement term.Finally it improves the inertia weight.Six classical functions are used to compare the tradi-tional particle swarm optimization algorithm,the simplified particle swarm optimization algorithm and the improved algorithm proposed in this paper.The experimental results show that the performance of the im-proved particle swarm optimization is better than the other two algorithms.