自适应免疫算法及其对动态函数优化的跟踪<sup>*</sup>

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Abstract ;An adaptive immune algorithm, based on the functions of the biological immune system such as adaptive learning, memory and surveillance, is proposed to solve high dimensional dynamical function optimization. In the algorithm, with structural simplicity, feasibility, and dynamical regulation of the execution time for different environments, the dynamical evolution and antibody rearrangement are involved. The dynamical memory pool consists of memory subsets related to the characteristics of immune memory and dynamical maintenance of the pool, in which each subset keeps some excellent memory cells obtained by the average linkage. In the meanwhile, dynamical surveillance and memory establish the environmental identification and generation rule of initial antibody populations. Experimental results and comparison illustrate the superiority and the effective tradeoff between performance effect and efficiency as well as the potential in the complex dynamical highdimensional optimization problems.

Key words： Dynamic Environment Function Optimization Environment Tracking High Dimensional Moving Peaks Immune Algorithm

Received: 31 December 2005

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	ZHANG ZhuHong
	QIAN ShuQu

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ZHANG ZhuHong,QIAN ShuQu. Adaptive Immune Algorithm and Its Track to Dynamic Function Optimization[J]. , 2007, 20(1): 85-94.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2007/V20/I1/85

[1] Dasgupta D, McGregor D R. Nonstationary Function Optimization Using the Structured Genetic Algorithm // Manner R, Manderick B, eds. Proc of the 2nd International Conference on Parallel Problem Solving from Nature. Belgium, Brussels, 1992: 145154
[2] Simes A, Costa E. Using GAs to Deal with Dynamic Environments: A Comparative Study of Several Approaches Based on Promoting Diversity // Proc of the Genetic and Evolutionary Computation Conference. New York, USA: Morgan Kaufmann, 2002: 913
[3] Yang Shengxiang. Constructing Dynamic Test Environments for Genetic Algorithms Based on Problem Difficulty // Proc of the IEEE Congress on Computation. Portland, USA, 2004, Ⅱ: 12621269
[4] Smith J E, Vavak F. Replacement Strategies in Steady State Genetic Algorithms: Dynamic Environments. Journal of Computing and Information Technology, 1999, 7(1): 4959
[5] Fogel L J, Owens A J, Walsh M J. Artificial Intelligence through Simulated Evolution. New York, USA: Wiley, 1966
[6] Cobb H G. An Investigation into the Use of Hypermutation as Adaptive Operator in Genetic Algorithms Having Continues, Time Dependent Nonstationary Environments. Technical Report, AIC90001, Washington, USA: Naval Research Laboratory, 1990
[7] Grefenstette J. Genetic Algorithms for Changing Environments // Proc of the 2nd International Conference on Parallel Problem Solving from Nature. Belgium, Brussels, 1992: 137144
[8] Oppacher F, Wineberg M. The Shifting Balance Genetic Algorithm: Improving the GA in a Dynamic Environment // Banzhaf W, Daida J, Eiben A E, et al, eds. Proc of the Genetic and Evolutionary Computation Conference. San Francisco, USA: Morgan Kaufmann, 1999: 504510
[9] Branke J. Memory Enhanced Evolutionary Algorithms for Changing Optimization Problems // Angeline P J, Michalewicz Z, Schoenauer M, et al, eds. Proc of the Congress on Evolutionary Computation. Washington, USA: IEEE Press, 1999, Ⅲ: 18751882
[10] Aragón V S, Esquivel S C. An Evolutionary Algorithm to Track Changes of Optimum Value Locations in Dynamic Environments. Journal of Computer Science and Technology, 2004, 4(3): 127134
[11] Gaspar A, Collard P. From GAs to Artificial Immune Systems: Improving Adaptation in Time Dependent Optimization // Angeline P J, Michalewicz Z, Schoenauer M, et al, eds. Proc of the Congress on Evolutionary Computation. Washington, USA, 1999, Ⅲ: 18591866
[12] Walker J H, Garrett S M. Dynamic Function Optimization: Comparing the Performance of Clonal Selection and Evolution Strategies // Proc of the 2nd International Conference on Artificial Immune Systems. Berlin, Germany: SpringerVerlag, 2003: 273284
[13] de Castro L N, Timmis J. Artificial Immune System: A New Computational Intelligence Approach. Berlin, Germany: SpringerVerlag, 2002
[14] Chung J S, Jung H K, Hahn S Y. A Study on Comparison of Optimization Performances between Immune Algorithm and Other Heuristic Algorithms. IEEE Trans on Magnetics, 1998, 34(5): 29722975
[15] Trojanowski K, Michalewicz Z. Evolutionary Optimization in NonStationary Environments. Journal of Computer Science and Technology, 2000, 1(2): 93124
[16] Luo Yinsheng, Li Renhou, Zhang Weixi. Dynamic Function Optimization Algorithm Based on Immune Mechanism. Journal of Xi’an Jiaotong University, 2005, 39(4): 384388 (in Chinese)
(罗印升, 李人厚, 张维玺. 基于免疫机理的动态函数优化算法. 西安交通大学学报, 2005, 39(4): 384388)
[17] Jin Y C, Branke J. Evolutionary Optimization in Uncertain Environments: A Survey. IEEE Trans on Evolutionary Computation, 2005, 9(3): 303317
[18] Zitzler E. Evolution Algorithms for Multiobjective Optimization: Methods and Application. Ph.D Dissertation. Zurich Swit, Zerland: Swiss Federal Institute of Technology, 1999
[19] Huang Xiyue, Zhang Zhuhong, He Chuangjiang, et al. Modern Intelligence Algorithms: Theory and Application. Beijing, China: Science Press, 2005 (in Chinese)
(黄席越, 张著洪, 何传江, 等. 现代智能算法理论及应用. 北京: 科学出版社, 2005)