基于不确定性和隔代遗传的矿用车平顺性优化

Ride Comfort Optimization of Mine Truck Based on Uncertainty Theory and IP-GA

建立了八自由度的矿用自卸车整车数学模型,并基于动态仿真模型构建设计变量和不确定变量与目标函数之间的近似模型,采用双层嵌套的隔代遗传算法,内层在不确定域上求解目标函数区间,外层对设计变量寻优,对该模型进行平顺性优化.最后与确定优化的结果进行比较,得出了不确定性优化的优越性.

A vehicle model of eight degrees of freedom was established. Then, based on dynamic simu- lation model, an approximate model between design variables, uncertain variables and objective function was built. Using double-nested IP-GA, the ride comfort of the model was improved. For IP-GA, the in- terval of objective function was obtained through the inner layer while the optimal variable was searched through the outer layer. Finally, the results were compared with those of certain optimization, which has shown the superiority of uncertain optimization.