基于量子计算模型的混合进化算法及其性能分析  

Hybrid Evolutionary Algorithm Based on Quantum Computing and Performance Analysis

作　　者：游晓明[1] 刘升[1] 王裕明[1] 

机构地区：[1]上海工程技术大学电子电气工程学院,上海201620

出　　处：《电子学报》2012年第4期856-860,共5页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61075115);上海市教委科研创新基金重点项目(No.12ZZ185)

摘　　要：提出了基于量子动力学机制的混合进化算法,该算法依据小生境机制将初始个体划分为实数编码染色体的子群,种群内部采用自适应算子搜索局域的最优解,种群之间则采用基于量子动力学机制的协同进化.混沌系统生成的初始染色体序列实际上并不完全随机,因此我们提出非对称区间产生混沌染色体序列并能生成更多的优秀个体.为解决二进制算法所不能避免的精度与效率的冲突,本文采用十进制编码染色体.利用量子动力学机制的高度分布并行性,本模型能更好的适应复杂的动态环境.我们不仅证明了算法的收敛性而且分析了提高算法性能的策略,仿真实验也验证了该算法的优越性.A novel Hybrid Evolutionary Algorithm based on Quantum computing（HQEA） is proposed.In the algorithm population is divided into subpopulations by niche methods.Each subpopulation can obtain optimal solution by the self-adaptive operator and all subpopulations can co-evolve by quantum dynamic mechanism.Initialization chaotic sequence is not as well as random in homogeneity,so this approach proposes strategy of unsymmetrical area to obtain optimal solution more.This approach adopts real-coded chromosome to solve precision and efficiency problem of binary system.The algorithm can adjust to dynamic environment more because of the distributed and parallel characteristic based on quantum dynamic mechanism.The convergence of the HQEA is proved and the strategy for improving the performance of HQEA has been analyzed.Its superiority is shown by some simulation experiments in this paper.

关 键 词：量子进化算法 量子动力学机制 协同进化 多峰函数优化 实数编码染色体 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]