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

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.