Method of Simulation and Intelligent Optimization for Tire Thread Noise Based on Adaptive Immune Genetic Algorithm

On the basis of the deficiencies of traditional immune genetic algorithm (IGA) in optimizing noise from tire thread, this thesis presents a new adaptive immune genetic algorithm (AIGA), which improves the genetic operator, provides adaptive crossover operator, mutation operator and reverse order operator, guarantees diversity of population, prevents precocity and promotes the ability of global search. Research was made on the method of intelligent optimization of noise from tire thread based on AIGA, and optimization simulation was carried combining specific tire thread pattern. Favorable result was acquired, which verified the effectiveness and practicability of this method.