用于全局优化问题的混合免疫进化算法

doi:10.3969/j.issn.1001-2400.2010.05.034

J4 ›› 2010, Vol. 37 ›› Issue (5): 971-980.doi: 10.3969/j.issn.1001-2400.2010.05.034

• 研究论文 • 上一篇

用于全局优化问题的混合免疫进化算法

刘星宝^1,2;蔡自兴¹;王勇¹;彭伟雄¹

(1. 中南大学信息科学与工程学院，湖南长沙 410083；
2. 湖南商学院现代教育技术中心，湖南长沙 410205)

收稿日期:2010-03-08 出版日期:2010-10-20 发布日期:2010-10-11
通讯作者: 刘星宝
作者简介:刘星宝(1977-)，男，中南大学博士研究生，E-mail: liuxb0608@gmail.com．
基金资助:
国家自然科学基金资助项目(90820302、60805027);国家博士点基金资助项目(200805330005);湖南省院士基金资助项目(2009FJ4030)

Hybrid immune evolutionary algorithm for global optimization problems

LIU Xing-bao^1,2;CAI Zi-xing¹;WANG Yong¹;PENG Wei-xiong¹

(1. School of Information Sci. and Eng., Central South Univ., Changsha 410083， China;
2. Center of Modern Edu. Tech., Hunan Univ. of Business, Changsha 410205， China)

Received:2010-03-08 Online:2010-10-20 Published:2010-10-11
Contact: LIU Xing-bao

摘要/Abstract

摘要：

为了克服免疫算法在优化高维多峰函数时存在的早熟收敛问题，提出一种高效的混合免疫进化算法．动态克隆扩张、基于学习机制的超变异和多母体交叉是该算法的主要特点．同时，提出了一种算法性能评价准则，以比较不同算法在优化高维函数时的性能．在实验部分，首先使用经典测试函数测试了混合免疫进化算法的性能;然后，分别在不同的评估次数下比较了自适应差分进化、基本免疫算法和混合免疫进化算法，结果表明免疫进化算法在求解精度、稳定性等方面均明显优于前两种算法．

关键词: 全局优化问题, 人工免疫系统, 克隆选择算法, 多母体随机交叉

Abstract:

In order to overcome the premature of the immune algorithm when solving high dimensional multimodal functions, an efficient hybrid immune evolutionary algorithm is proposed. The main characteristics of the novel hybrid algorithm are dynamic clonal selection, learning-based hypermutation and multi-parentic crossing operators. In addition, a novel performance evaluation criterion for comparing different algorithms is constructed. In an experimental study, firstly the performance of the proposed HIEA is validated using several classical test functions; next HIEA is compared with self-adaptive differential evolution (SaDE) and a simple immune algorithm (SIA) under a certain amount of function evaluations, experimental results show that the performance of the proposed HIEA is significantly better than that of SaDE and SIA in terms of accuracy and stability.

Key words: global optimization problems, artificial immune systems, clonal selection algorithm, multi-parentic crossing operator

刘星宝;蔡自兴;王勇;彭伟雄. 用于全局优化问题的混合免疫进化算法[J]. J4, 2010, 37(5): 971-980.

LIU Xing-bao;CAI Zi-xing;WANG Yong;PENG Wei-xiong. Hybrid immune evolutionary algorithm for global optimization problems[J]. J4, 2010, 37(5): 971-980.

参考文献

[1] Hunt J E, Cooke D E. An Adaptive, Distributed Learning System Based on Immune System[C] //IEEE International Conference on System, Man and Cybernetics: Vol 3. Vancouver: IEEE Press, 1995: 2494-2499.
[2] 王磊, 潘进, 焦李成. 免疫规划[J] . 计算机学报, 2000, 23(8): 806-812.
Wang Lei, Pan Jin, Jiao Licheng. The Immune Programming[J] . Chinese Journal of Computers, 2000, 23(8): 806-812.
[3] De Castro L N, Von Zuben F J. The Clonal Selection Algorithm with Engineering Application[C] //Proc of the Genetic and Evolutionary Computation Conf on Workshop on Artificial Immune System and Their Applications. Las Vegas: Morgan Kaufmann Publishers, 2000: 36-37
[4] Bernardino H S. A New Hybrid AIS-GA for Constrained Optimization Problems in Mechanical Engineering[C] //2008 IEEE Congress on Evolutionary Computation: Vol 1-8. Hongkong: IEEE, 2008: 1455-1462.
[5] Li J L, Cai Z X, Chen H. Immunity Clone Algorithm with Particle Swarm Evolution[J] . Journal of Central South University of Technology, 2006, 13(6): 703-706.
[6] Wang J, Zhang X H, Jiao L C. Integrated the Simplified Interpolation and Clonal Selection Into the Particle Swarm Optimization for Optimization Problems[C] //Simulated Evolution and Learning, Proceedings. Berlin: Springer, 2006: 433-440.
[7] Dai H W. Quantum Interference Crossover-Based Clonal Selection Algorithm and Its Application to Traveling Salesman Problem[J] . Ieice Trans on Information and Systems, 2009, E92d(1): 78-85.
[8] Goncalves R A. A Cultural Immune System for Economic Load Dispatch with Non-smooth Cost[C] //Artificial Immune Systems, Proceedings. Berlin: Springer, 2007: 382-394.
[9] Gao S. A Hybrid Clonal Selection Algorithm[J] . International Journal of Innovative Computing Information and Control, 2008, 4(4): 995-1008.
[10] Karakasis V K, Stafylopatis A. Efficient Evolution of Accurate Classification Rules Using a Combination of Gene Expression Programming and Clonal Selection[J] . IEEE Trans on Evolutionary Computation, 2008, 12(6): 662-678.
[11] Cutello V, Nicosia G. An Immunological Approach to Combinatorial Optimization Problems[C] //Proc 8th Ibero-American Conf Artif Intell. Seville: Springer, 2002: 361-370.
[12] Cutello V, Nicosia G. An Immune Algorithm for Protein Structure Prediction on Lattice Models[J] . IEEE Trans on Evolutionary Computation, 2007, 11(1): 101-117.
[13] 杜海峰, 公茂果, 刘若辰, 等. 自适应混沌克隆进化规划算法[J] . 中国科学E辑(信息科学)，2005, 35(8): 817-829.
Du Haifeng, Gong Maoguo, Liu Ruochen, et al. Self-adaptive Chaos Clonal Evolutionary Programming[J] . Science in China Ser E(Information Sciences), 2005, 35(8): 817-829.
[14] 公茂果, 焦李成, 杜海峰, 等. 用于约束优化的人工免疫响应进化策略[J] . 计算机学报, 2007, 30(1): 37-47.
Gong Maoguo, Jiao Licheng, Du Haifeng, et al. Novel Evolutionary Strategy Based on Artificial Immune Response for Constrained Optimizations[J] . Chinese Journal of Computers, 2007, 30(1): 37-47.
[15] 刘若辰, 贾建, 赵梦玲, 等. 一种免疫记忆动态克隆策略算法[J] . 控制理论与应用, 2007, 24(5): 777-784.
Liu Ruochen, Jia Jian, Zhao Mengling, et al. An Immune Memory dynamic Clonal strategy Algorithm[J] . Control Theory & Applications, 2007, 24(5): 777-784.
[16] Nitesh K, Anoop P, Ravi S, et al. Fast Clonal Algorithm[J] . Engineering Applications of Artificial Intelligence, 2008, 21(1): 106-128.
[17] Yao X, Liu Y. Evolutionary Programming Made Faster[J] . IEEE Trans on Evolutionary Computation, 1999, 3(2): 82-102.

用于全局优化问题的混合免疫进化算法

Hybrid immune evolutionary algorithm for global optimization problems

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