Immune algorithm with biologic information clone
采用生物信息克隆的免疫算法

The clonal selection process depending on a homeostasis network is closely linked with genetic factors. Inspired by this immunology principle, an immune algorithm with biologic information clone(IABIC) is proposed. To deal with the missing information in traditional artificial clonal selection process, this evolutionary algorithm introduces the environment information, history information of antibody and hereditary characteristics into the artificial immune system, which serves as the prior knowledge to provide decision support for clonal selection process and conduct the rebirth of antibody system. Meanwhile, hybrid coding was adopted to improve the diversity of the population and the convergence speed, and then, the global convergence of this algorithm was analyzed. Simulation results indicate that this clonal strategy makes significant improvement in optimizing the ability of immune clonal algorithm. Compared with other immune clonal optimization algorithms and evolution algorithms, it has higher precision, faster convergence, remarkable convergence character for higher dimensional optimization, and the ability for avoiding premature convergence.