Locally weighted regression models for surrogate-assisted design optimization

DOI10.1007/s11081-017-9370-5zbMath1391.90683OpenAlexW2591485304MaRDI QIDQ724346

Sébastien Le Digabel, Bastien Talgorn, Michael Kokkolaras, Charles Audet

Publication date: 25 July 2018

Published in: Optimization and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11081-017-9370-5

zbMATH Keywords

derivative-free optimizationlocal regressionsurrogate modelsMADSorder error

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Applications of mathematical programming (90C90) Nonlinear programming (90C30) Topological methods for optimization problems in solid mechanics (74P15)

Related Items

Number of DoE Required for Estimating Different Quality Surrogate, Quantifying uncertainty with ensembles of surrogates for blackbox optimization, GOSAC: global optimization with surrogate approximation of constraints, Two decades of blackbox optimization applications, Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints, Dynamic improvements of static surrogates in direct search optimization

Uses Software

• NOMAD
• DAKOTA
• LOWESS

Cites Work

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