Multi-objective Optimization Of Vehicle Occupant Restraint System By Using Evolutionary Algorithm With Response Surface Model
Multi-objective Optimization Of Vehicle Occupant Restraint System By Using Evolutionary Algorithm With Response Surface Model
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DOI：
10.2495/cmem-v5-n2-163-170
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
H. Horii
中科院分区：
文献类型：
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作者：
H. Horii
本研究以响应面模型为基础，应用演化多目标最佳化方法进行汽车乘员约束系统的设计。汽车乘员约束系统由安全气囊、安全带、膝垫等约束设备组成。优化的目的是通过评估一些指标的基础上一些安全规则，以提高系统的安全性。为加快优化速度，引入了安全指标的估算模型。利用机器学习方法中的高斯过程来构造估计模型，即响应面模型。高斯过程从采样结果构建估计模型，通过多体动力学仿真计算。通过对Pareto最优解的分析，得到了约束系统安全性能的权衡信息和设计变量对安全性能的贡献等对约束系统设计有帮助的信息。
This research reports a vehicle occupant restraint system design by using evolutionary multi-objective optimization with response surface model. The vehicle occupant restraint systems are composed of restraint equipment, such as an airbag, a seat belt and a knee bolster. The optimization aims to improve the safety of the system by evaluating some indexes based on some safety regulations. Estimation models of the safety indexes are introduced for accelerating the optimization. The estimation models, which are called the response surface models, are constructed by using Gaussian Process, which is a kind of machine learning method. The Gaussian Process constructs the estimation model from sampling results, which are calculated by using multi-body dynamics simulation. Some helpful information for designing the restraint systems, such as trade-off information of safety performance and contribution of design variables for the safety performance, is obtained by analysing the Pareto optimal solutions.