Abstract: In order to reduce the production costs and enhance the fuel economy of a company's 4×2 livestock and poultry transport vehicle, on the premise of ensuring the structural performance of the livestock and poultry vehicle, lightweight design of the compartment was carried out. Static and dynamic analysis of the compartment were conducted to understand its structural performance. A mixed sensitivity method was used to select 30 design variables from 80, followed by Hammersley sampling for these variables. An approximate model was created by using mobile least squares regression (MLSR), and its fitting accuracy was determined by the coefficient of determination. The model was optimized by using a multi‑objective genetic algorithm (MOGA) to obtain the Pareto frontier. Based on gray correlation analysis, a set of optimal plate thicknesses was selected from the Pareto frontier. The optimized model resulted in a 6.8% reduction in compartment weight while maintaining overall structural performance, and the first‑order modal frequency increased by 2.9 Hz.

Key words: livestock and poultry vehicle carriage, approximate model, multi?objective optimization, Pareto frontier, grey correlation degree

摘要： 为降低某公司4×2畜禽运输车的生产成本以及提高车辆的燃油经济性，在保证车厢结构性能的前提下，对畜禽车车厢进行轻量化设计。通过对车厢进行静态分析和动态分析，了解其结构性能后，使用混合灵敏度方法从80组设计变量中筛选出30组设计变量，再用哈默斯雷法Hammersley对设计变量进行采样；随后使用移动最小二乘法MLSR创建近似模型，并通过确定系数来判断拟合精度；最后使用多目标遗传算法MOGA对近似模型进行优化并得到帕雷托前沿，并基于灰色关联度分析从该帕雷托前沿中筛选出一组最优板厚。优化后的模型在保证车厢整体结构性能的前提下使车厢减重6.8%，并且一阶模态频率提高2.9 Hz。

关键词: 畜禽车车厢, 近似模型, 多目标优化, 帕雷托前沿, 灰色关联度