Probability-based least square support vector regression metamodeling technique for crashworthiness optimization problems

Authors:
Hu Wang;Enying Li;G. Y. Li
Affiliations:
Key Laboratory of Advanced Technology for Vehicle Body Design and Manufacture, College of Mechanical and Automobile Engineering, Hunan University, Changsha, People's Republic of China 410082;School of Logistics, Central South University of Forestry and Teleology, Changsha, People's Republic of China 41004;Key Laboratory of Advanced Technology for Vehicle Body Design and Manufacture, College of Mechanical and Automobile Engineering, Hunan University, Changsha, People's Republic of China 410082
Venue:
Computational Mechanics
Year:
2011

Citing 5
Cited 2

Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Support Vector Machine Regression for Volatile Stock Market Prediction

IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Development of metamodeling based optimization system for high nonlinear engineering problems

Advances in Engineering Software
A comparative study of metamodeling methods for multiobjective crashworthiness optimization

Computers and Structures
Travel-time prediction with support vector regression

IEEE Transactions on Intelligent Transportation Systems

Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models

Structural and Multidisciplinary Optimization
A prior-knowledge input LSSVR metamodeling method with tuning based on cellular particle swarm optimization for engineering design

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a crashworthiness design optimization method based on a metamodeling technique. The crashworthiness optimization is a highly nonlinear and large scale problem, which is composed various nonlinearities, such as geometry, material and contact and needs a large number expensive evaluations. In order to obtain a robust approximation efficiently, a probability-based least square support vector regression is suggested to construct metamodels by considering structure risk minimization. Further, to save the computational cost, an intelligent sampling strategy is applied to generate sample points at the stage of design of experiment (DOE). In this paper, a cylinder, a full vehicle frontal collision is involved. The results demonstrate that the proposed metamodel-based optimization is efficient and effective in solving crashworthiness, design optimization problems.