Optimization design of hybrid chaos immune algorithm with self-adaptive parameter adjusting

Abstract

In order to improve the performance of immune algorithm, chaos optimization is integrated into immune clone selection algorithm. Portion antibodies after decoding are mapped into Lozi's chaos field, and then collect every optical value by each of chaos iteration is added into the new antibody group to improve the antibody-antigen fitness value. This paper analyzes the importance of the selection probability of antibody and gives its varied calculated equation, and adopts the changeable rate of fitness by antibodies 3 times continuous generations to regulate antibody selection probability value adaptively, and gives the concrete steps for the optimization method of hybrid chaos immune algorithm with self-adaptive parameter adjusting. To compare the performance of optimization method of hybrid chaos immune algorithm with self-adaptive parameter adjusting, immune clone selection algorithm and traditional method in the literature, the three methods are used to optimize the main beam cross-section for crane structure. Comparison results indicate that the optimization method of hybrid chaos immune algorithm with self-adaptive parameter adjusting has many advantages such as better self-adaptive capacity, higher computation efficiency and design accuracy.