%0 Journal Article
%T Improved quantum genetic algorithm based on cloud model theory<br>一种基于云模型的改进型量子遗传算法*
%A XU Bo
%A PENG Zhi-ping
%A YU Jian-ping
%A <br>许波
%A 彭志平
%A 余建平
%J 计算机应用研究
%D 2011
%I 
%X Quantum genetic algorithm for optimization in function easily falls into local optimal solution and the premature quickly converges of such shortcomings. This paper improved the use of cloud models of quantum genetic algorithm, using quantum cloud-to-species evolution of gene populations and qualitative operation of the control and quantum based on cloud model adaptive strategy revolving door update operation, so that qualitative knowledge of the algorithm could be adaptive control under the guidance of the scope of the search space, and their best under the conditions of the larger search space to avoid the local optimal solution. A typical function of comparative experiment results show that the algorithm can avoid trapping in local optimal solution, and enhance the ability of global optimization at the same time be able to more quickly converge to the global optimal solution. The quality and efficiency of optimization is better than the genetic algorithm and quantum genetic algorithm.
%K cloud model
%K quantum computing
%K quantum genetic algorithm(QGA)
%K function optimization<br>云模型
%K 量子计算
%K 量子遗传算法
%K 函数优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=1F8EB868F38CE07253B9ABE12CE9C42D&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=F3090AE9B60B7ED1&sid=F19AD5ADFE708A86&eid=B703A46FA75824E1&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=14