一种动态改变权值的简化粒子群算法

A Modified Simple Particle Swarm Optimization Using Dynamically Decreasing Inertia Weight

基本粒子群优化算法(bPSO)具有容易陷入局部极值、进化后期收敛速度慢、精度低等缺陷,而舍弃了速度项的简化粒子群算法(sPSO)在保证了收敛速度和精度的同时使算法更加简练。文中提出了一种动态改变权值的简化粒子群算法。并经实验证明,该算法在搜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 decreasing inertia weight strategy in this article is presented. It is demonstrated that there are evident superiorities in computational precision, searching speed and steady convergence.