全局粒子群优化算法

针对粒子群优化算法在解决大维数的无约束优化问题时具有较差的收敛性和稳定性,提出了一种全局粒子群优化(GPSO)算法.GPSO算法引入了一种新的惯性权重,它被定义为一个指数型函数与一个随机数的乘积,这有利于维持算法的全局搜索和局部搜索.同时,GPSO算法对全局最优解进行了小的扰动,这可以有效地避免算法早熟.使用三种粒子群优化算法来解决6个无约束优化问题.仿真结果说明,与其他两种粒子群优化算法相比,GPSO算法具有更快的收敛速度和更强的逃离局部最优的能力.

A Global Particle Swarm Optimization Algorithm

Particle swarm optimization(PSO) algorithm shows good performance on solving small-scale unconstrained optimization problem,however,it has poor convergence and stability on solving large-scale ones.In order to improve the performance of the PSO algorithms,a global particle swarm optimization(GPSO) algorithm was proposed.The GPSO introduces a new inertia weight,and it is defined as the product of an exponential type function and a random number,which is beneficial to keeping the global and local searching ca...