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.
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.
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)
