摘要
微粒群算法中微粒有保持自身状态的特性,如何改变其状态对微粒位置和速度的调整有较大的影响,本文给出一种周期性随机扰动的自适应改变微粒速度的方法。当微粒要进行下一次运动时,总体采用非线性下降的惯性权重选择方法,并且在其中加入周期性随机扰动策略,使算法既能得到较快的收敛速度,又不至于陷入局部极值。将此方法应用于对几个标准函数的测试中,与标准微粒群算法及只采用线性下降的微粒群算法进行比较,新方法能得到更好的结果。
Particle, among swam algorithm, is apt to keep its own state. While how to change its state has great influence on the position and the adjustment of the velocity. In this paper presents a new method-an adaptive particle swarm algorithm of periodic random disturbance strategy. And the nonlinear declination as well as Self-adapting inertia improved in the process of particles moving. Better results can be obtained by the new method compared with the former ones and which only adopts linear decline in the oarticle swarm algorithrn.
出处
《计算机系统应用》
2011年第6期203-206,共4页
Computer Systems & Applications
基金
周口师范学院青年科研基金(zknuqn201039A)
关键词
微粒群算法
自适应
随机扰动
惯性权重
particle swarm optimization
adaptive
random disturbance
self-adapting inertia
