减速器
托普西斯
数学优化
序列二次规划
粒子群优化
多目标优化
帕累托原理
理想溶液
遗传算法
启发式
分类
工程类
计算机科学
二次规划
算法
数学
运筹学
物理
土木工程
热力学
作者
Xipeng Xu,Xinbo Chen,Zhe Liu,Jun Yang,Yanan Xu,Yong Zhang,Yunkai Gao
标识
DOI:10.1080/0305215x.2021.1923704
摘要
In this study, a novel multi-objective reliability-based design optimization (MORBDO) method considering the maximum allowable deviation range of design variables is proposed for the reducer housing of electric vehicles. First, the numerical model of the reducer housing is established by ABAQUS and verified by experiments. A radial basis function (RBF) neural network model is used to construct the approximate finite element model. The structural parameters of the RBF are optimized using the heuristic global optimization ability of the particle swarm optimization (PSO) algorithm. Sequential quadratic programming (SQP) and non-dominated sorting genetic algorithm II (NSGA II) are used to perform the MORBDO. Finally, the technique for order preference by similarity to ideal solution, a multi-criteria decision-making (MCDM) method, is used to select the ideal design in multi-objective Pareto points. The optimization method generated a set of Pareto non-dominated solutions with three objectives, which can be selected for a more feasible scheme using MCDM. The proposed method comprehensively measures the requirements of manufacturing and performance criteria, and the optimization results provide a variety of optimization design schemes for the reducer housing of electric vehicles.
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