基于熵权-改进TOPSIS法的驾驶室多目标优化  

Cab multi-objective optimization based on entropy weight-improved TOPSIS method

作　　者：彭雪梅 刘夫云[1] 孙永厚[1] 汤金帅 PENG Xuemei;LIU Fuyun;SUN Yonghou;TANG Jinshuai(School of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guilin 541004)

机构地区：[1]桂林电子科技大学机电工程学院,广西桂林541004

出　　处：《机械设计》2023年第11期122-127,共6页Journal of Machine Design

基　　金：广西新能源物流商用车协同创新研发及成果转化应用项目(桂科AA18242036);广西研究生教育创新计划项目(2022YCXS013)。

摘　　要：为了提升商用车轻量化优化效果,文中提出了一种基于熵权-改进逼近理想解排序法(TOPSIS)的驾驶室多目标决策方法。首先,对驾驶室有限元模型进行性能分析,并通过试验模态与仿真模态进行对比,验证了模型的准确性;其次,采用区域灵敏度分析,筛选出20个厚度和4个截面形状变量,并建立2阶响应面近似模型验证其精度;最后,采用第三代非劣排序遗传算法(NSGA-Ⅲ)对驾驶室进行多目标优化设计,再联合熵权-改进TOPSIS法求得非支配帕累托解的相对贴进度,并以此作为多目标决策的最终结果。结果表明:与优化前相比较,驾驶室质量减小了24.9 kg,减幅达到8.1%,能够满足轻量化和性能需求。In this article,for lightweight optimization of commercial vehicles,a cab multi-objective decision-making method is proposed based on entropy weight-improved TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution).Firstly,the performance of the cab's finite-element model is analyzed;it is verified that this model is accurate by comparing the experimental modal with the simulated modal.Secondly,the regional-sensitivity analysis is used to select 20 thickness and 4 section shape variables;a second-order response-surface approximation model is set up to verify the accuracy.Finally,the third-generation non-inferior sorting genetic algorithm(NSGA-II)is used to carry out the multi-objective optimization design on the cab;the entropy weight-improved TOPSIS method is used to obtain the relative schedule of the non-dominated Pareto solution,as the final result of multi-objective decision-making.The results show that the cab weight,compared with its counterpart before optimization,has reduced by 24.9 kg,which is a decrease of 8.1%,thus meeting the requirements of lightweight design and performance.

关 键 词：试验模态 区域灵敏度 多目标优化 熵权-改进TOPSIS法 

分 类 号：U463.81[机械工程—车辆工程]