Immune Optimization Algorithm based on MHC Regulation

The protein major histocompatibility complex (MHC) plays an important role in immune systems, the benefit of the MHC polymorphism in immune response is due to its critical influence on the selection and evolution of the antibody. This paper presents an immune optimization algorithm based on the MHC regulation function (IOAMHC) in immune responses. The work presented here build upon previous evolutionary algorithm and clonal selection principle for optimization. In the IOAMHC, the MHC is used to guide the evolution of the antibody, so as to accelerate optimization. The experiment results on the traveling salesman problem (TSP) show that the IOAMHC has much higher convergence speed and better optimization results than that of classical optimization algorithms. The performance of the IOAMHC parameters has also been discussed in this paper