Efficient Kriging-based robust optimization of unconstrained problems

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Keywords: Robust optimization, Expected Improvement, Worst-case design, Implementation error, Efficient Global Optimization. Abstract: In this paper, we use Kriging and expected improvement to apply robust optimization on unconstrained problems affected by implementation error. A two-stage process is employed, where, at the first stage, a response surface of the nominal function is fitted using a design of experiments strategy such as Latin hypercube sampling (LHS). Based on this response surface, in each iteration, we construct a worst-case cost metamodel by finding the maximum realizable value of the objective with respect to the uncertainty set on the nominal metamodel. We use the total Kriging error estimate of the two metamodels to find an appropriate expected improvement criterion for robust optimization. A new sample is added at each iteration by finding the location at which this modified expected improvement measure is maximum. By means of this process, we iteratively move towards the robust optimum. We test the efficiency and convergence of the algorithm by performing hundred runs of the considered test problem for different initial sampling. These results show that the algorithm converges to the robust optimum consistently.
Original languageEnglish
Title of host publicationProceedings 3rd International Conference on Simulation and Modelling Methodologies, Technologies and Applications
EditorsT Oren, J Kacprzyk, LP Leifsson, et al
Place of PublicationReykjavik, Iceland
Number of pages9
ISBN (Print)978-989-8565-69-3
Publication statusPublished - 2013
EventSIMULTECH 2013, Reykjavik, Iceland - Reykjavik, Iceland
Duration: 29 Jul 201331 Jul 2013

Publication series



ConferenceSIMULTECH 2013, Reykjavik, Iceland

Bibliographical note

Onder dezelfde titel in een aangepaste versie in 2014 in het tijdschrift Journal for Computational Science verschenen


Dive into the research topics of 'Efficient Kriging-based robust optimization of unconstrained problems'. Together they form a unique fingerprint.

Cite this