Clonal Strategy Algorithm Based on the Immune Memory
Clonal Strategy Algorithm Based on the Immune Memory作者机构：Institute of Intelligent Information Processing Xidian University Xi＇an 710071 P.R. China Department of Mathematics Northwest University Xi＇an 710069 P.R. China
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2005年第20卷第5期
页 面：728-734页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：国家自然科学基金 国家重点基础研究发展计划(973计划)
主 题：artificial immune system clonal selection, immune memory evolutionary computation, traveling salesman problem
摘 要：Based on the clonal selection theory and immune memory mechanism in the natural immune system, a novel artificial immune system algorithm, Clonal Strategy Algorithm based on the Immune Memory （CSAIM）, is proposed in this paper. The algorithm realizes the evolution of antibody population and the evolution of memory unit at the same time, and by using clonal selection operator, the global optimal computation can be combined with the local searching. According to antibody-antibody （Ab-Ab） affinity and antibody-antigen （Ab-Ag） affinity, the algorithm can allot adaptively the scales of memory unit and antibody population. It is proved theoretically that CSAIM is convergent with probability 1. And with the computer simulations of eight benchmark functions and one instance of traveling salesman problem （TSP）, it is shown that CSAIM has strong abilities in having high convergence speed, enhancing the diversity of the population and avoiding the premature convergence to some extent.
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