Immune optimization algorithm based on fuzzy logic and chaos theory and its application

Traditional immune algorithm overcomes the defect of premature convergence, it keep the diversity of population in big range search space, but has slow convergence speed in small scope, and the manual experience values of crossover rate and mutation rate can directly affect the performance of optimization algorithm. Chaos algorithm can get more accurate optimal result in small scope because of the ergodicity, values of crossover rate and mutation rate can be fine tuned by fuzzy system because of its uncertainty and adaptability. In order to overcome the shortage of traditional immune algorithm, this paper, by analyzing the model of Heat-setting machine, proposes an immune algorithm based on fuzzy logic and chaos theory. The simulation results show that, compared with the immune algorithm based on chaos theory and the traditional immune algorithm, the immune algorithm based on fuzzy logic and chaos theory evidently improves the convergence speed, has good performance and much practical value because of higher precision and stronger stability.