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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 model and support vector machine (MORO-KS) is proposed in this work. First, the main problem in MORO-KS is iteratively restricted by constraint cuts formed in the subproblem. Second, each objective function is approximated by a Kriging model to predict the response value. Third, a support vector machine (SVM) classifier is constructed to replace all constraint functions classifying design alternatives into two categories: feasible and infeasible. The proposed MORO-KS approach is tested on two numerical examples and the design optimization of a micro-aerial vehicle (MAV) fuselage. Compared with the results obtained from other MORO approaches, the effectiveness and efficiency of the proposed MORO-KS approach are illustrated.<\/jats:p>","DOI":"10.1115\/1.4040710","type":"journal-article","created":{"date-parts":[[2018,7,20]],"date-time":"2018-07-20T22:18:20Z","timestamp":1532125100000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":28,"title":["Advanced Multi-Objective Robust Optimization Under Interval Uncertainty Using Kriging Model and Support Vector Machine"],"prefix":"10.1115","volume":"18","author":[{"given":"Tingli","family":"Xie","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Jiang","sequence":"additional","affiliation":[{"name":"Professor The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leshi","family":"Shu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahui","family":"Zhang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangzheng","family":"Meng","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Zhengzhou University, Zhengzhou 450001, China e-mail:"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2018,8,6]]},"reference":[{"issue":"2","key":"2019100314525481000_bib1","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.jmsy.2013.12.009","article-title":"A Robust Optimization Approach for Pollution Routing Problem With Pickup and Delivery Under Uncertainty","volume":"33","year":"2014","journal-title":"J. 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