Adaptive Immune Genetic Algorithm for Tire Tread Pattern Pitch Parameters Optimization

In order to reduce tire tread pattern noise, an adaptive immune genetic algorithm (AIGA) is presented to optimize tire pitch parameters in this paper. According to character of tire pattern, the algorithm defines crossover, mutation and reverse order operation to improve searching ability. The multiple parameters optimization is discussed in this paper. The simulation results indicate that compared with genetic algorithm (GA) and immune genetic algorithm (IGA), the convergence and the efficiency of AIGA are distinctly improved. The optimized results can reduce tread patterns noise level, which has been applicable to development of tire thread pattern.

I. Introduction

Tire/road noise is generated by several sub-sources, such as tread vibration, air pumping, slip-stick, adhesion et al. [1]. Therefore tread pattern noise is the main noise, which is the important link of low noise tire design. The common design method usually cannot guarantee low noise tire. We must optimize tire thread pattern in order to scatter the frequency of the pattern noise to reduce noise level [2]–[3].