非线性随机扰动的粒子群优化算法

A particle swarm optimization algorithm based on nonlinear random disturbance

在惯性权重非线性递减策略的基础上,引入小阻尼振荡函数,提出一种新的非线性递减随机扰动的粒子群算法,通过2个基准测试函数对算法性能和收敛性进行了分析.实验仿真表明:相对于标准粒子群算法,新策略加快了收敛速度,在一定程度上避免了粒子群优化算法的早熟收敛问题.

This paper introduced the small damping oscillation function based on the nonlinear decreasing inertia weight strategy, and presented a new nonlinear decreasing random perturbation of particle swarm optimization algorithm, and conducted performance testing of the algorithm through the two benchmark test functions. Simulation results indicate that new strategy improve the algorithm speed of convergence, and avoid the premature convergence of particle swarm optimization relative to the standard particle swarm algorithm.