Print Email Facebook Twitter Expected improvement based infill sampling for global robust optimization of constrained problems Title Expected improvement based infill sampling for global robust optimization of constrained problems Author Rehman, S.U. (TU Delft Structural Optimization and Mechanics) Langelaar, M. (TU Delft Structural Optimization and Mechanics) Date 2017 Abstract A novel adaptive sampling scheme for efficient global robust optimization of constrained problems is proposed. The method addresses expensive to simulate black-box constrained problems affected by uncertainties for which only the bounds are known, while the probability distribution is not available. An iterative strategy for global robust optimization that adaptively samples the Kriging metamodel of the computationally expensive problem is proposed. The presented approach is tested on several benchmark problems and the average performance based on 100 runs is evaluated. The applicability of the method to engineering problems is also illustrated by applying robust optimization on an integrated photonic device affected by manufacturing uncertainties. The numerical results show consistent convergence to the global robust optimum using a limited number of expensive simulations. Subject Efficient global optimizationExpected improvementKrigingRobust optimization To reference this document use: http://resolver.tudelft.nl/uuid:50d919e3-ba70-4768-92f3-31471bcf58c0 DOI https://doi.org/10.1007/s11081-016-9346-x ISSN 1389-4420 Source Optimization and Engineering, 18 (3), 723-753 Part of collection Institutional Repository Document type journal article Rights © 2017 S.U. Rehman, M. Langelaar Files PDF art_10.1007_s11081_016_9346_x.pdf 1.31 MB Close viewer /islandora/object/uuid:50d919e3-ba70-4768-92f3-31471bcf58c0/datastream/OBJ/view