Recent advances in surrogate-based optimization
AIJ Forrester, AJ Keane - Progress in aerospace sciences, 2009 - Elsevier
The evaluation of aerospace designs is synonymous with the use of long running and
computationally intensive simulations. This fuels the desire to harness the efficiency of …
computationally intensive simulations. This fuels the desire to harness the efficiency of …
Special section on multidisciplinary design optimization: metamodeling in multidisciplinary design optimization: how far have we really come?
The use of metamodeling techniques in the design and analysis of computer experiments
has progressed remarkably in the past 25 years, but how far has the field really come? This …
has progressed remarkably in the past 25 years, but how far has the field really come? This …
[BOG][B] Design and analysis of simulation experiments
JPC Kleijnen - 2018 - Springer
This contribution summarizes the design and analysis of experiments with computerized
simulation models. It focuses on two metamodel (surrogate, emulator) types, namely first …
simulation models. It focuses on two metamodel (surrogate, emulator) types, namely first …
Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO
This manuscript reviews recent advances in deterministic global optimization for Mixed-
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …
Infill sampling criteria for surrogate-based optimization with constraint handling
JM Parr, AJ Keane, AIJ Forrester… - Engineering …, 2012 - Taylor & Francis
This article discusses the benefits of different infill sampling criteria used in surrogate-based
constrained global optimization. A new method which selects multiple updates based on …
constrained global optimization. A new method which selects multiple updates based on …
Quantile-based optimization of noisy computer experiments with tunable precision
This article addresses the issue of kriging-based optimization of stochastic simulators. Many
of these simulators depend on factors that tune the level of precision of the response, the …
of these simulators depend on factors that tune the level of precision of the response, the …
Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction
V Picheny - Statistics and Computing, 2015 - Springer
Optimization of expensive computer models with the help of Gaussian process emulators is
now commonplace. However, when several (competing) objectives are considered …
now commonplace. However, when several (competing) objectives are considered …
Blind Kriging: Implementation and performance analysis
When analysing data from computationally expensive simulation codes or process
measurements, surrogate modelling methods are firmly established as facilitators for design …
measurements, surrogate modelling methods are firmly established as facilitators for design …
Revisiting Bayesian optimization in the light of the COCO benchmark
It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for
optimizing numerically costly functions. However, BO is not often compared to widely …
optimizing numerically costly functions. However, BO is not often compared to widely …
Simultaneous kriging-based estimation and optimization of mean response
J Janusevskis, R Le Riche - Journal of Global Optimization, 2013 - Springer
Robust optimization is typically based on repeated calls to a deterministic simulation
program that aim at both propagating uncertainties and finding optimal design variables …
program that aim at both propagating uncertainties and finding optimal design variables …