A surrogate-based cooperative optimization framework for computationally expensive black-box problems
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Publication:2218881
DOI10.1007/s11081-020-09526-7zbMath1457.90152OpenAlexW3040357768MaRDI QIDQ2218881
Ricardo García-Ródenas, José Carlos García-García, Esteve Codina
Publication date: 18 January 2021
Published in: Optimization and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11081-020-09526-7
radial basis functionsexpected improvementblack-box functioncooperative optimizationparallel surrogate-based optimization
Nonlinear programming (90C30) Approximation methods and heuristics in mathematical programming (90C59)
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Cites Work
- Constrained global optimization of expensive black box functions using radial basis functions
- A method for simulation based optimization using radial basis functions
- SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems
- Computational intelligence in expensive optimization problems
- Improved strategies for radial basis function methods for global optimization
- Parallel radial basis function methods for the global optimization of expensive functions
- Efficient global optimization of expensive black-box functions
- Extensions of Dinkelbach's algorithm for solving nonlinear fractional programming problems
- Algorithmic construction of optimal symmetric Latin hypercube designs.
- Efficient global optimization algorithm assisted by multiple surrogate techniques
- Pseudo expected improvement criterion for parallel EGO algorithm
- A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions
- Parallel Stochastic Global Optimization Using Radial Basis Functions
- Surrogate‐based methods for black‐box optimization
- On Some Properties of Programming Problems in Parametric form Pertaining to Fractional Programming
- Global optimization
- A radial basis function method for global optimization
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