The Research On Artificial Immune System Theory And Immune Clone Optimization Algorithm

The vertebrate immune system is a highly evolutionary system, which is highly adaptive, highly distributed and self-organizing. It can effectively recognize the antigens and kill them rapidly to protect the stability of the body. The Artificial Immune System (AIS) is an intelligent information processing technology that is based on the mechanism of the vertebrate immune system. As a novel branch of computational intelligence, AIS has strong capabilities of pattern recognition, learning and associative memory, hence it is natural to view AIS as a powerful information processing and problem-solving paradigm in both the scientific and engineering fields.This paper intends to give a comprehensive overview of AIS based on a preliminary theoretical framework, which is started with the brief interpretative introduction of biological models of vertebrate immune system, then followed with some extracted bionic principles, via immune recognition, immune learning, immune memory, clonal selection, diversity generation and maintenance etc. The Shape-Space model of AIS and the Artificial Immune Network (AIN) model based on binary-coding are detailed subsequently. Then some AIS models that are representative are introduced, such as AIS model based on negative selection principle, AIS and AIN models based on clonal selection principle.On the analysis of the clonal selection mechanism, an Immune Clonal Algorithm based on clonal selection principle (ICACS) is presented in this paper. The antibody recombination-mutation operator, clonal deletion operator, antibody complement operator are introduced. The first two operators are employed to enhance and maintain the diversity of the antibody population; the third operator is useful to prevent the antibody from degeneration. ICACS is applied to solving the Generalized Minimum Spanning Tree (GMST) and the Logistics Distribution Vehicle Routing Problem. The simulation result shows that ICACS is can find the global optimal solution more rapidly and reliably.Finally, A novel Immune Clonal Algorithms base on clustering and competition (ICACC) is presented. The antibody-suppressing mechanism is also import to the ICACC. These two mechanisms enhance the diversity of the antibody population greatly. The method of calculating the distance between the antibodies based on information entropy is introduced in the binary-coding model. And the double-mutation operator composed of Gauss mutation operator and Cauchy operator is also employed. Then the ICACC is applied to optimizing the comprehensive test function . The simulation result shows that ICACC can converge to the global optimal solution reliably and rapidly and can avoid the premature phenomena in the...