Adaptive Immune Genetic Algorithm and its application in PID parameter optimization for main steam temperature control system

Abstract

Aiming at prematureness, slow convergence rate and reduction in diversity which exist in Genetic Algorithm (GA), this paper presents Adaptive Immune Genetic Algorithm (AIGA) based on GA and immune system mechanism. Adaptive Immune Genetic Algorithm introduces antigens recognition function, immune memory function and antibodies self-adjusting function to Genetic Algorithm, and replaces the fixed probability crossover and mutation operator of Genetic Algorithm with the adaptive probability crossover and mutation operator. AIGA overcomes some disadvantages of GA, such as prematureness, slow convergence speed and reduction in diversity. And AIGA has strong global optimization ability and high searching efficiency. Then AIGA is used to optimize PID parameter for the main steam temperature control system. The simulation comparison experiment with different methods shows that PID parameters obtained by AIGA may provide better control effect than those obtained by GA and the engineering tuning methods. That is, the system control effect adopting AIGA-PID parameter has small overshoot, short adjusting time, and smooth transition. The simulation result also proves the validity of AIGA.