Multi-Response Weighted Adaptive Sampling Approach Based on Hybrid Surrogate Model

Jiangqi Long,Ping Yu,Yaoqing Liao

doi:10.1109/access.2021.3066475

Jiangqi Long, Ping Yu + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3066475

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Abstract

In order to improve the fitting accuracy and optimization efficiency of the surrogate model, a multi-response weighted adaptive sampling (MWAS) approach based on the hybrid surrogate model was proposed and implemented to a multi-objective lightweight design of car seats. In this approach, the sample discreteness index in the input design space was calculated by the maximum and minimum distance approach (MDA), the fitting uncertainty index of output response was calculated by a strategy based on the weighted prediction variance (WPV), and the two indices are combined by the weight coefficients. In the iterative process, the weight coefficients of the two indices were determined according to the accuracy of the hybrid surrogate model. The balance of global and local accuracy was realized by considering the sample dispersion and the fitting uncertainty of the surrogate model comprehensively. Numerical examples of single-response and multi-response systems showed that the proposed approach has excellent sampling efficiency and robustness. Moreover, the results of actual engineering application showed that the hybrid surrogate model constructed through MWAS could significantly improve the efficiency of model optimization. Hence, a high-precision optimization solution to the multi-objective lightweight design of passenger car rear seat was obtained.

Highlights

Safety, energy conservation and environmental friendliness are the three major themes of the development of the automobile industry
The error between the optimized solution of each response and its simulation value were below 4.5%, indicated that the hybrid surrogate model based on the multi-response weighted adaptive sampling (MWAS) method has a better accuracy guarantee
With the intention to improve the accuracy of the surrogate model and optimize efficiency, in this article a weighted prediction variance (WPV) approach based on the hybrid surrogate model was proposed to identify areas with large predicted deviations, and a MWAS approach was established by combining it with minimum distance approach (MDA)

Summary

INTRODUCTION

Energy conservation and environmental friendliness are the three major themes of the development of the automobile industry. In the field of multi-objective engineering optimization research, the accuracy of the surrogate model largely depends on the number of sample points and their location distribution on the design space [18]. A multi-response weighted adaptive sampling (MWAS) approach based on the hybrid surrogate model is proposed. This approach comprehensively considers the dispersion of sample points in the input design space and the fitting uncertainty of output response. The weight coefficients are adaptively selected according to the accuracy of the hybrid surrogate model Using this approach for sampling the design space, the sampling efficiency and the fitting accuracy of the surrogate model can be improved effectively. They were sorted according to the GMSE, and the three single surrogate models with the highest fitting accuracy were weighted to construct a hybrid surrogate model

EXPERIMENTAL DESIGN

ADAPTIVE SAMPLING SCHEME

MULTI-OBJECTIVE OPTIMIZATION

Findings

CONCLUSION