基于混沌映射的元胞遗传算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201501006

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摘要针对元胞遗传算法(CGA)的功能及结构特点，将元胞遗传算法与混沌算法进行有机结合，分别设计基于Cat映射、Logistic映射及Tent映射的混沌映射元胞遗传算法(CCGA)，并解释三种映射的遍历性.文中利用混沌映射的遍历特点及初值敏感性优化种群的初始分布，扩大搜索范围，设计遗传算子中的局部混沌交叉操作及混沌变异扰动机制，并比较不同混沌映射算子作用下种群多样性的变化.理论分析及计算机仿真实验表明，引入三种混沌映射的元胞遗传算法在提升寻优精度，提高算法收敛速度，避免局部极值方面均取得良好的效果.

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	李雪岩
	李雪梅
	李学伟
	吴今培

关键词 ：元胞遗传算法(CGA), 种群分布, 局部混沌交叉, 混沌变异, 混沌映射

Abstract：According to the function and structure characteristics of cellular genetic algorithm (CGA), chaos cellular genetic algorithm (CCGA) based on Cat map, Logistic map and Tent map are designed respectively with the organic combination of cellular genetic algorithm and chaos algorithm. Besides, the ergodicity of three chaotic mappings are explained. Taking advantage of chaotic ergodicity and sensitivity to initial condition, the initial distribution of population is optimized, the searching scope of the algorithm is enlarged, the mechanism of local chaotic crossover operator and chaotic mutation disturbance are designed, and the changes of population diversity are compared under different mapping operators. Theoretical analysis and simulation results show that the proposed algorithm has obtained good performance in improving optimizing accuracy, accelerating convergence and avoiding the local optimum by introducing three chaotic maps.

Key words： Cellular Genetic Algorithm(CGA) Population Distribution Local Chaotic Crossover Chaotic Mutation Chaotic Map

收稿日期: 2013-08-30

ZTFLH:

TP 301.6

基金资助:国家自然科学基金项目(No.71273023)、高等学校博士学科点专项科研基金项目(No.20130009110020)资助

作者简介: 李雪岩，男，1987年生，博士研究生，主要研究方向为管理科学与复杂系统决策理论方法.E-mail:gongye1632006@163.com.李雪梅(通讯作者)，女，1967年生，教授，博士生导师，主要研究方向为运输经济理论与政策.E-mail: xmli@bjtu.edu.cn.李学伟，男，1962年生，教授，博士生导师，主要研究方向为管理科学与复杂系统决策理论方法.吴今培，男，1937年生，教授，博士生导师，主要研究方向为智能信息处理.

引用本文:

李雪岩，李雪梅，李学伟，吴今培. 基于混沌映射的元胞遗传算法^*[J]. 模式识别与人工智能, 2015, 28(1): 42-49. LI Xue-Yan, LI Xue-Mei, LI Xue-Wei, Wu Jin-Pei. Cellular Genetic Algorithm Based on Chaotic Map. , 2015, 28(1): 42-49.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201501006 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2015/V28/I1/42

[1] Li J H, Li M. Convergence Analysis and Convergence Rate Estimate of Cellular Genetic Algorithms.Pattern Recognition and Artificial Intelligence, 2012, 25(5): 874-878 (in Chinese)
(李军华,黎明.元胞遗传算法的收敛性分析和收敛速度估计.模式识别与人工智能, 2012, 25(5): 874-878)
[2] Foong H H, Leow S K, Ong T J. Probabilistic Selection in Cellular Genetic Algorithm // Proc of the 8th IEEE International Conference on Natural Computation. Chongqing, China, 2012: 688-692
[3] Morales-Reyes A, Erdogan A T.Internal Lattice Reconfiguration for Diversity Tuning in Cellular Genetic Algorithms.PLoS One, 2012, 7(7):1-19
[4] Kirley M.A Cellular Genetic Algorithm with Disturbance:Optimization Using Dynamic Spatial Interactions.Journal of Heuristics, 2002, 8(3): 321-342
[5] Lu Y M, Li M, Li L. The Cellular Genetic Algorithm with Evolutionary Rule. Acta Electronica Sinica, 2010, 38(7): 1603-1607 (in Chinese)
(鲁宇明,黎明,李凌.一种具有演化规则的元胞遗传算法.电子学报, 2010, 38(7): 1603-1607)
[6] Lu Y M, Cai Y, Li M.Hierarchical Cellular Genetic Algorithm Based on Polycentric Urban Strategy.Journal of Computer Applications, 2011, 31(12): 3309-3311 (in Chinese)
(鲁宇明,蔡晔,黎明.基于多中心城市策略的分层元胞遗传算法.计算机应用, 2011, 31(12): 3309-3311)
[7] Chen S, Lu Y M, Yang H Y, et al.Selection Pressure Study of Cellular Genetic with Disturbances. Computer Engineering and Applications, 2011, 47(27): 32-35,97 (in Chinese)
(陈殊,鲁宇明,杨红雨,等.灾变机制下元胞遗传算法的选择压力研究.计算机工程与应用, 2011, 47(27): 32-35,97)
[8] Li M, Wang Y, Chen H, et al.Cellular Genetic Algorithm Based on a Predator and Prey Mechanism.Journal of Applied Sciences-Electronics and Information Engineering, 2012, 30(6): 669-676 (in Chinese)
(黎明,王莹,陈昊,等.基于捕食机制的元胞遗传算法.应用科学学报, 2012, 30(6): 669-676)
[9] Al-Naqi A,Erdogan A T,Arslan T.Dynamic Fault-Tolerant Three-dimensional Cellular Genetic Algorithms.Journal of Parallel and Distributed Computing, 2013, 73(2): 122-136
[10] Al-Naqi A,Erdogan A T, Arslan T, et al. Balancing Exploration and Exploitation in an Adaptive Three-Dimensional Cellular Genetic Algorithm via a Probabilistic Selection Operator // Proc of the NASA/ESA Conference on Adaptive Hardware and Systems. Anaheim, USA, 2010: 258-264
[11] Ma Z S. Chaotic Populations in Genetic Algorithms. Applied Soft Computing, 2012, 12(8): 2409-2424
[12] Xiang W L, An M Q.An Efficient and Robust Artificial Bee Colony Algorithm for Numerical Optimization. Computers & Operations Research, 2013, 40(5): 1256-1265
[13] Du H F,Gong M G,Liu R C,et al.Self-Adaptive Chaos Clone Evolutionary Programming Algorithm. Science in China Series E: Information Sciences, 2005, 35(8): 817-829 (in Chinese)
(杜海峰,公茂果,刘若辰,等.自适应混沌克隆进化规划算法.中国科学E辑:信息科学, 2005, 35(8): 817-829)
[14] Zhao X M. Research on Chaotic Time Series Forecasting and Chaotic optimization.Ph.D Dissertation. Hangzhou, China: Zhejiang University, 2002 (in Chinese)
(赵小梅.混沌预测与混沌优化理论与算法研究.博士学位论文.杭州:浙江大学, 2002)
[15] Wang F, Dai Y S,Wang S S. Modified Chaos-Genetic Algorithm. Computer Engineering and Applications, 2010, 46(6): 29-32 (in Chinese)
(王芳,戴永寿,王少水.改进的混沌遗传算法.计算机工程与应用, 2010, 46(6): 29-32)
[16] Li M, Jie L L, Lu Y M.A Hybrid Particle Swarm and Multi-population Cellular Genetic Algorithm.Pattern Recognition and Artificial Intelligence, 2012, 25(4): 610-616 (in Chinese)
(黎明,揭丽琳,鲁宇明.粒子群与多种群元胞遗传混合优化算法.模式识别与人工智能, 2012, 25(4): 610-616)