ARGONAUT: algorithms for global optimization of constrained grey-box computational problems. The algorithmic framework ARGONAUT is presented for the global optimization of general constrained grey-box problems. ARGONAUT incorporates variable selection, bounds tightening and constrained sampling techniques, in order to develop accurate surrogate representations of unknown equations, which are globally optimized. ARGONAUT is tested on a large set of test problems for constrained global optimization with a large number of input variables and constraints. The performance of the presented framework is compared to that of existing techniques for constrained derivative-free optimization.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Audet, Charles; Côté, Pascal; Poissant, Catherine; Tribes, Christophe: Monotonic grey box direct search optimization (2020)
- Bajaj, Ishan; Hasan, M. M. Faruque: Global dynamic optimization using edge-concave underestimator (2020)
- Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
- Kieslich, Chris A.; Boukouvala, Fani; Floudas, Christodoulos A.: Optimization of black-box problems using Smolyak grids and polynomial approximations (2018)
- Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)