Immune Co-evolution Algorithm based on Chaotic Optimization

This paper combines the advantages of chaos theory, co-evolution algorithm and immune algorithm, and proposes a new hybrid evolutionary algorithm: chaotic immune co-evolution algorithm (CICA). CICA on the basis of the traversal and internal randomicity of the chaos theory, the memory and diversity of the biological immunity and the mechanism of cooperative evolution in the nature can effectively overcome the shortcomings of genetic algorithm, such as the lack of convergence efficiency and local optimization. This paper sets up a CICA model, designs and describes the main flow of this algorithm. More important, we simulate and test the CICA using the standard testing function and bier- 127 TSP. Compared the results with those of the other hybrid evolutionary algorithms, we find that CICA can promise the global optimization and high convergence efficiency, more effective than genetic algorithm and artificial immune algorithm.