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ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems

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Abstract

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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Acknowledgments

The authors acknowledge financial support from the National Science Foundation (CBET-0827907, CBET-1263165).

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Authors and Affiliations

  1. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA

    Fani Boukouvala & Christodoulos A. Floudas

  2. Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, USA

    Fani Boukouvala & Christodoulos A. Floudas

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  1. Fani Boukouvala
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  2. Christodoulos A. Floudas
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Correspondence to Christodoulos A. Floudas.

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Boukouvala, F., Floudas, C.A. ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems. Optim Lett 11, 895–913 (2017). https://doi.org/10.1007/s11590-016-1028-2

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