A surrogate-model-based method for constrained optimization

By Charles Audet, J. Denni, Douglas Moore, A. Booker, Paul D. Frank. 2000

238 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “Since EGO’s publication, there has evolved a body of work devoted to extending the algorithm, particularly in adding constraints to the optimization problem [6], [131], [23], and in modelling noisy functions [14], [75], [76].”
Using Cross Validation to Design Conservative Surrogates (2010, methodology): “There are different ways to address this issue: for example, 1) using conservative constraints [13–15] so that the optimization is pushed to the feasible region and 2) making the limit statemore accurate near the boundary between of the feasible domain (usually through iterative update of the surrog”
Trailing-edge noise reduction using derivative-free optimization and large-eddy simulation (2007, methodology): “An important modification of the SMF method is the addition of constraints in a rigorous manner using a filter; constraints were added to pattern-search methods in this way by Audet & Dennis (2004b) and Audet et al. (2000).”
Tunability: Importance of Hyperparameters of Machine Learning Algorithms (2018, methodology): “Bergstra and Bengio (2012) compute the relevance of the hyperparameters of neural networks and conclude that some are important on all datasets, while others are only important on some datasets. Their conclusion is primarily visual and used as an argument for why random search works better than grid”
A review on design, modeling and applications of computer experiments (2006, methodology): “Finally, Booker (1998), Booker et al. (1998) and Audet et al. (2000) describe an optimization framework that is driven by approximate models and numerical results for helicopter rotor design.”
On the Design of Optimization Strategies Based on Global Response Surface Approximation Models (2005, methodology): “One method for modifying the EI criterion, termed expected violation, is discussed by Audet et al. (2000).”
Modeling an augmented Lagrangian for improved blackbox constrained optimization (2014, background): “This literature is focused primarily on the objective f , with far less attention to constraints (e.g., Audet et al., 2000; Sasena, 2002).”
Approximation methods in multidisciplinary analysis and optimization: a panel discussion (2004, methodology): “Global optimization procedures using kriging models were discussed (Booker et al. 1999; Sasena et al. 2002; Audet et al. 2000 ), and a procedure for calibrating a kriging model during optimization that avoided problems with an ill-conditioned correlation matrix was discussed by Booker (2000b), see ”
Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization (2002, methodology): “Better ways to handle the constraints may require completely new approaches such as those suggested by Audet et al. [2].”
An experimental methodology for response surface optimization methods (2011, methodology): “For example, one might wish to impose general constraints of the form g(x) < 0 on the solution, where g(x) is also modelled using a Gaussian process (Schonlau 1997; Audet et al. 2000; Sasena 2002).”
Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping (2008, background): “[21] Received 7 December 2007; revision received 4 April 2008; accepted for publication 5May 2008.”
Predictive quantification of surrogate model fidelity based on modal variations with sample density (2015, background): “In the literature, there exist various criteria for determining the locations of the infill points (Jones et al. 1998; Williams et al. 2011; Keane 2006; Booker et al. 1999; Audet et al. 2000).”
Sequential kriging optimization using multiple-fidelity evaluations (2006, background): “Audet et al. (2000) calculated the so-called “expected violation” of the constraints, before the EI criterion is considered.”
Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations. (2002, methodology): “researchers have performed similar Bayesian analysis studies that gather several new sample points per iteration [7], [41].”
Mixed Variable Optimization of a Load-Bearing Thermal Insulation System Using a Filter Pattern Search Algorithm (2004, result): “Several studies have shown success using this approach (Audet et al., 2000; Booker et al., 1998, 1999a, 1999b).”