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Received:October 11, 2010 Revised:November 17, 2010
Received:October 11, 2010 Revised:November 17, 2010
中文摘要: 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。
Abstract: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 particle swarm algorithm.
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基金项目:周口师范学院青年科研基金(zknuqn201039A)
引用文本：
于来行,乔蕊.周期性扰动的微粒群算法.计算机系统应用,2011,20(6):203-206
YU Lai-Hang,QIAO Rui.Particle Swarm Algorithm of Periodic Random Disturbance.COMPUTER SYSTEMS APPLICATIONS,2011,20(6):203-206
于来行,乔蕊.周期性扰动的微粒群算法.计算机系统应用,2011,20(6):203-206
YU Lai-Hang,QIAO Rui.Particle Swarm Algorithm of Periodic Random Disturbance.COMPUTER SYSTEMS APPLICATIONS,2011,20(6):203-206
