Optimal Strategy Research Based On Artificial Immune Algorithm

An improved immune genetic algorithm is presented by research of Artificial Immune System (AIS) basing on the principle of Biological Immune System (BIS) in this paper. The design of the improved immune genetic algorithm is analyzed and its convergence is approved. And, an ant genetic algorithm based on immune mechanism is presented by research of Ant Colony System (ACS) according to the principle of ant looking for food. This algorithm is also analyzed. The detail work is as follows:First, using density-regulating mechanism, individual diversity retaining strategy and immune memory function and introducing the niche isolation mechanism, the improved immune genetic algorithm based on the adaptive selection probability of concentration and fitness is proposed. The algorithm can effectively overcome immature convergence phenomenon in Simple Genetic Algorithm (SGA). It can improve not only antibody's similarity but also its diversity. And it can avoid local optimal solution and shorten searching time. At the same time this paper presents a general expressing form of this kind of the algorithms.Second, the immune genetic idea is applied in the idea of ant looking for food, and then the ant genetic algorithm based on immune mechanism is put forward. On the premise of reserving the good characteristics that are globalization characteristic and rationality of answer in limit time in primary algorithm, the diversity of individuals can be assured and the phenomenon of long searching time and easily stagnating is avoided. And, the design of the ant genetic algorithm based on immune mechanism is analyzed.Last, the improved immune genetic algorithm and the ant genetic algorithm based on immune mechanism are applied to the problems of function optimization. Optimizing several non-linearity functions proves the validity of two algorithms. Through the compare among algorithms, it is shows that two algorithms have faster convergent speed.