自适应免疫遗传算法在轮胎花纹节距参数优化中的研究

Study on tire tread pattern pitch parameter optimization based on adaptive immune genetic algorithm

节距参数优化是低噪声轮胎花纹优化设计的关键环节,针对轮胎花纹节距参数优化寻优时易引发数据爆炸的特点,利用自适应免疫遗传算法(AIGA)的自适应策略和快速搜索能力对节距序列参数和节距比例参数进行优化分析,结果表明该算法的收敛性和效率较遗传算法和免疫遗传算法都有明显的提高,优化结果可降低轮胎花纹噪声水平,具有工程应用价值。

Pitch parameter optimization is the key in low noise tire tread patterns optimization design.In view of easy data explosion when optimizing tread patterns pitch,adaptive immune genetic algorithm (AIGA) was used,which is based on adaptive strategy and has fast searching ability to carry out the optimization analysis on the pitch sequence parameter and the pitch proportion parameter.Finally results indicate that the convergence and the efficiency of the algorithm are distinctly enhanced as compared with the genetic algorithm and immune genetic algorithm.The optimuization results can reduce tread patterns noise level,and are of project application value.