0
research-article

Advanced Multi-Objective Robust Optimization under Interval Uncertainty Using Kriging and Support Vector Machine

[+] Author and Article Information
Tingli Xie

The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science & Technology, 430074 Wuhan, P.R. China
xietingli0727@gmail.com

Ping Jiang

Professor, The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science & Technology, 430074 Wuhan, P.R. China
jiangping@hust.edu.cn

Qi Zhou

Assistant Professor, School of Aerospace Engineering, Huazhong University of Science & Technology, 430074 Wuhan, P.R. China
qizhouhust@gmail.com

Leshi Shu

The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science & Technology, 430074 Wuhan, P.R. China
leshishu@gmail.com

Yahui Zhang

The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science & Technology, 430074 Wuhan, P.R. China
zyhzhangzhang@gmail.com

Xiangzheng Meng

The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science & Technology, 430074 Wuhan, P.R. China
mxz761201@163.com

Hua Wei

School of Mechanical Engineering, Zhengzhou University, 450001 Zhengzhou, P.R. China
weihua0319@gmail.com

1Corresponding author.

ASME doi:10.1115/1.4040710 History: Received February 05, 2018; Revised June 27, 2018

Abstract

There are a large number of real-world engineering design problems with multi-objective, multi-constraint and having uncertainty in their inputs. Robust optimization is developed to obtain solutions that are optimal and less sensitive to uncertainty. Since most of complex engineering design problems rely on time-consuming simulations, the robust optimization approaches may become computationally intractable. To address this issue, an advanced multi-objective robust optimization approach based on Kriging and support vector machine (MORO-KS) is proposed in this work. Firstly, the main problem in MORO-KS is iteratively restricted by constraint cuts formed in the sub-problem. Secondly, a Support Vector Machine (SVM) model is constructed to replace all constraint functions classifying design alternatives into two categories: feasible and infeasible. Thirdly, each objective function is approximated by a Kriging model to predict the response value. The proposed MORO-KS approach is tested on two numerical examples and the design optimization of a micro-aerial vehicle fuselage. Compared with the results obtained from the MORO approach based on Constraint Cuts (MORO-CC), the effectiveness and efficiency of the proposed MORO-KS approach are illustrated.

Copyright (c) 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In