Surrogate‐based methods for black‐box optimization
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Publication:5278217
DOI10.1111/itor.12292zbMath1366.90196OpenAlexW2338548332MaRDI QIDQ5278217
Claudia D'Ambrosio, Leo Liberti, Youssef Hamadi, K. H. Vu
Publication date: 13 July 2017
Published in: International Transactions in Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/itor.12292
Nonlinear programming (90C30) Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59)
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Uses Software
Cites Work
- Exploratory designs for computational experiments
- Global optimization of expensive black box problems with a known lower bound
- Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for~computationally expensive black-box global optimization problems
- SO-MI: a surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems
- Constrained global optimization of expensive black box functions using radial basis functions
- Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions
- A method for simulation based optimization using radial basis functions
- Mixture surrogate models based on Dempster-Shafer theory for global optimization problems
- SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications
- Improved strategies for radial basis function methods for global optimization
- Parallel radial basis function methods for the global optimization of expensive functions
- Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection
- An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
- Convergence properties of the expected improvement algorithm with fixed mean and covariance functions
- Finding maximin Latin hypercube designs by iterated local search heuristics
- A review of statistical models for global optimization
- Efficient global optimization of expensive black-box functions
- Application of Bayesian approach to numerical methods of global and stochastic optimization
- The design and analysis of computer experiments.
- Algorithmic construction of optimal symmetric Latin hypercube designs.
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Metamodeling: Radial basis functions, versus polynomials
- A taxonomy of global optimization methods based on response surfaces
- Global optimization of costly nonconvex functions using radial basis functions
- Polynomial interpolation in several variables
- Engineering design applications of surrogate-assisted optimization techniques
- Application of a particle swarm optimization algorithm for determining optimum well location and type
- Efficient global optimization algorithm assisted by multiple surrogate techniques
- A quasi-multistart framework for global optimization of expensive functions using response surface models
- Space-filling Latin hypercube designs for computer experiments
- Centered $L_2$-discrepancy of random sampling and Latin hypercube design, and construction of uniform designs
- A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions
- Algorithm 909
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- On the Convergence of Pattern Search Algorithms
- Introduction to Derivative-Free Optimization
- A Pattern Search Filter Method for Nonlinear Programming without Derivatives
- Use of quadratic models with mesh-adaptive direct search for constrained black box optimization
- A Progressive Barrier for Derivative-Free Nonlinear Programming
- Mesh Adaptive Direct Search Algorithms for Constrained Optimization
- A Simplex Method for Function Minimization
- A radial basis function method for global optimization
