%0 Journal Article
%T Quantum-behaved particle swarm optimization based on Gaussian disturbance<br>基于高斯扰动的量子粒子群优化算法
%A WANG Xiao-gen
%A LONG Hai-xia
%A SUN Jun
%A <br>王小根
%A 龙海侠
%A 孙俊
%J 计算机应用研究
%D 2010
%I 
%X Due to shortcoming of quantum-behaved particle swarm optimization (QPSO) algorithm that it was often premature convergence, this paper proposed a revised QPSO with Gaussian disturbance on the mean best position or global best position of the swarm. The disturbance could effectively prevent the stagnation of the particles and therefore made them escape the local optima more easily. To evaluate the performance of the new method, tested the QPSO with Gaussian disturbance, along with QPSO and standard PSO on several well-known benchmark functions. Experiment simulations show that the proposed algorithm has powerful optimizing ability and more quickly convergence speed.
%K quantum-behaved particle swarm optimization algorithm
%K mean position
%K global best position
%K Gaussian distur-bance<br>量子粒子群优化算法
%K 平均位置
%K 全局最优位置
%K 高斯扰动
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=619570464E450AB816C246C347302978&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=B31275AF3241DB2D&sid=7361423B3D179EDD&eid=572ABCACB4426B6D&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=16