一种动态改变学习因子的简化粒子群算法

A Modified Simple Particle Swarm Optimization Using Dynamically Changing Learning Factor

基本粒子群优化算法(basic particle swarm optimization,简称bPSO)具有容易陷入局部极值,进化后期熟练速度慢,精度低等缺陷,而简化粒子群算法(simple particle swarm optimization,简称sPSO)在保证了熟练速度和精度的同时舍弃了速度项,使算法更加简练。本文提出了一种动态改变学习因子的简化粒子群算法。经过实验证明,该算法在寻优精度和收敛速度上具有明显的优势。

The basic particle swarm optimization （bPSO） has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. The simple PSO discards the particle velocity and improves extraordinarily the convergence velocity and precision in the evolutionary optimization, and it looks more legible and terse. A modification to dynamically changing learning factor strategy in this article is presented. It demonstrates that there are evident superiorities in computational precision, searching speed and steady convergence.