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 ar...

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Bibliographic Details
Published inOptimization letters Vol. 11; no. 5; pp. 895 - 913
Main Authors Boukouvala, Fani, Floudas, Christodoulos A.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2017
Subjects
Computational Intelligence
Mathematics
Mathematics and Statistics
Numerical and Computational Physics
Operations Research/Decision Theory
Optimization
Original Paper
Simulation
General constraints
Nonlinear programming
Surrogate modeling
Grey-box optimization
Variable selection
Derivative-free optimization
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Summary: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.
ISSN:1862-4472
1862-4480
DOI:10.1007/s11590-016-1028-2