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• W2955908957 abstract "We introduce the algorithm SHEBO (surrogate optimization of problems with hidden constraints and expensive black-box objectives), an efficient optimization algorithm that employs surrogate models to solve computationally expensive black-box simulation optimization problems that have hidden constraints. Hidden constraints are encountered when the objective function evaluation does not return a value for a parameter vector. These constraints are often encountered in optimization problems in which the objective function is computed by a black-box simulation code. SHEBO uses a combination of local and global search strategies together with an evaluability prediction function and a dynamically adjusted evaluability threshold to iteratively select new sample points. We compare the performance of our algorithm with that of the mesh-based algorithms mesh adaptive direct search (MADS, NOMAD [nonlinear optimization by mesh adaptive direct search] implementation) and implicit filtering and SNOBFIT (stable noisy optimization by branch and fit), which assigns artificial function values to points that violate the hidden constraints. Our numerical experiments for a large set of test problems with 2–30 dimensions and a 31-dimensional real-world application problem arising in combustion simulation show that SHEBO is an efficient solver that outperforms the other methods for many test problems." @default.
• W2955908957 created "2019-07-12" @default.
• W2955908957 creator A5055960651 @default.
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• W2955908957 date "2019-10-01" @default.
• W2955908957 modified "2023-10-16" @default.
• W2955908957 title "Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints" @default.
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• W2955908957 doi "https://doi.org/10.1287/ijoc.2018.0864" @default.
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