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Optimization Letters

, Volume 11, Issue 5, pp 895–913 | Cite as

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

  • Fani Boukouvala
  • Christodoulos A. Floudas
Original Paper

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.

Keywords

Grey-box optimization Surrogate modeling Variable selection Derivative-free optimization General constraints Nonlinear programming 

Notes

Acknowledgments

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

Supplementary material

11590_2016_1028_MOESM1_ESM.doc (340 kb)
Supplementary material 1 (doc 340 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Artie McFerrin Department of Chemical EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Texas A&M Energy InstituteTexas A&M UniversityCollege StationUSA

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