计算机工程2013，Vol.39Issue(7)：247-251,256,6.DOI:10.3969/j.issn.1000-3428.2013.07.055
基于变异概率分析的改进QGA及其应用
Improved QGA Based on Mutation Probability Analysis and Its Application
摘要
Abstract
Standard Quantum Genetic Algorithm(QGA) is premature convergence to local optima when it is applied to combinatorial optimization.To solve this problem,this paper analyzes the mutation probability distribution of Q-bit by introducing the k bit variation subspace conception and points out the conflict of traditional random mutation mechanism and the QGA self-implied variation mechanism.Based on these analysis,a novel Stage Large-scale Variation Mechanism Based on Observation(SLVMBOO) is proposed.Mutation operator of SLVMBOO which is embedded in the quantum rotation policy table is simple to implement and it is highly efficient.The tests results of different scale of 0/1 knapsack problem show that this mechanism can effectively avoid the premature convergence and successfully jump out of local optima when it is applied to combinatorial optimization.The global optimization ability is superior to the standard QGA.关键词
量子计算/量子遗传算法/变异机制/变异概率分布/组合优化/0/1背包问题Key words
quantum computation/ Quantum Genetic Algorithm(QGA)/ mutation mechanism/ mutation probability distribution/combinatorial optimization/ 0/1 knapsack problem分类
信息技术与安全科学引用本文复制引用
戴勇谦,张明武,祝胜林,戴勇新..基于变异概率分析的改进QGA及其应用[J].计算机工程,2013,39(7):247-251,256,6.基金项目
国家自然科学基金资助项目(61272404) （61272404）
