Kriging-based infill sampling criterion for constraint handling in multi-objective optimization

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

zbMATH Keywords

krigingmulti-objective optimizationconstrained optimizationderivative-free optimizationcomputer-intensive

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Derivative-free methods and methods using generalized derivatives (90C56)

Related Items (6)

Methods for constrained optimization of expensive mixed-integer multi-objective problems, with application to an internal combustion engine design problem ⋮ Gradient enhanced Gaussian process regression for constitutive modelling in finite strain hyperelasticity ⋮ SMGO-\(\Delta\): balancing caution and reward in global optimization with black-box constraints ⋮ Expected improvement for expensive optimization: a review ⋮ A survey on kriging-based infill algorithms for multiobjective simulation optimization ⋮ Efficient reliability analysis based on adaptive sequential sampling design and cross-validation

Uses Software

• DIRECT
• DACE
• SPEA2
• EGO
• ParEGO
• SMS-EMOA

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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