Print Email Facebook Twitter Efficient global robust optimization of unconstrained problems affected by parametric uncertainties Title Efficient global robust optimization of unconstrained problems affected by parametric uncertainties Author ur Rehman, S. Langelaar, M. Faculty Mechanical, Maritime and Materials Engineering Department Precision and Microsystems Engineering Date 2015-05-13 Abstract A novel technique for efficient global robust optimization of problems affected by parametric uncertainties is proposed. The method is especially relevant to problems that are based on expensive computer simulations. The globally robust optimal design is obtained by searching for the best worst-case cost, which involves a nested min-max optimization problem. In order to reduce the number of expensive function evaluations, we fit response surfaces using Kriging and use adapted versions of expected improvement to direct the search for the robust optimum. The numerical performance of the algorithm is compared against other techniques for min-max optimization on established test problems. The proposed approach exhibits reliable convergence, is more efficient than previous methods and shows strong scalability. Subject krigingmin-max optimizationexpected improvementrobust optimizationparametric uncertaintiesworst-case design To reference this document use: http://resolver.tudelft.nl/uuid:c2e74325-30f8-4bf6-8a76-9cc2f044be8a Publisher Springer ISSN 1615-147X Source https://doi.org/10.1007/s00158-015-1236-x Source Structural and Multidisciplinary Optimization, 52, 2015 Part of collection Institutional Repository Document type journal article Rights © 2015 The Author(s)This article is published with open access at Springerlink.com Files PDF Rehman 2015.pdf 1.58 MB Close viewer /islandora/object/uuid:c2e74325-30f8-4bf6-8a76-9cc2f044be8a/datastream/OBJ/view