Kriging-based infill sampling criterion for constraint handling in multi-objective optimization
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Publication:905759
DOI10.1007/s10898-015-0370-8zbMath1339.90306OpenAlexW1786052414MaRDI QIDQ905759
David Herrero-Pérez, Jesús Martínez-Frutos
Publication date: 28 January 2016
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10898-015-0370-8
krigingmulti-objective optimizationconstrained optimizationderivative-free optimizationcomputer-intensive
Multi-objective and goal programming (90C29) Derivative-free methods and methods using generalized derivatives (90C56)
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Uses Software
Cites Work
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- Survey of multi-objective optimization methods for engineering
- A modified DIRECT algorithm with bilevel partition
- Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization
- Constrained global optimization of expensive black box functions using radial basis functions
- SMS-EMOA: multiobjective selection based on dominated hypervolume
- Kriging metamodeling in simulation: a review
- Efficient global optimization of expensive black-box functions
- Lipschitzian optimization without the Lipschitz constant
- The design and analysis of computer experiments.
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- A taxonomy of global optimization methods based on response surfaces
- A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization
- Exploiting Hessian matrix and trust-region algorithm in hyperparameters estimation of Gaussian process
- Scalarizing cost‐effective multi‐objective optimization algorithms made possible with kriging
- Multiobjective Optimization
- Multi-Objective Optimization Using Surrogates
- Evolutionary Algorithms for Solving Multi-Objective Problems
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