Constrained efficient global optimization with support vector machines

DOI10.1007/s00158-011-0745-5zbMath1274.90271OpenAlexW2093304293MaRDI QIDQ381828

Anirban Basudhar, Sylvain Lacaze, Christoph Dribusch, Samy Missoum

Publication date: 15 November 2013

Published in: Structural and Multidisciplinary Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00158-011-0745-5

zbMATH Keywords

classificationconstrained optimizationsupport vector machinesdiscontinuitiesbinary problemsefficient global optimization

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (12)

On \texttt{MATLAB} experience in accelerating \texttt{DIRECT-GLce} algorithm for constrained global optimization through dynamic data structures and parallelization ⋮ GOSAC: global optimization with surrogate approximation of constraints ⋮ Adaptive virtual support vector machine for reliability analysis of high-dimensional problems ⋮ A complete expected improvement criterion for Gaussian process assisted highly constrained expensive optimization ⋮ Learning Enabled Constrained Black-Box Optimization ⋮ A kriging-based constrained global optimization algorithm for expensive black-box functions with infeasible initial points ⋮ pBO-2GP-3B: a batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics ⋮ Expected improvement for expensive optimization: a review ⋮ Sequential model based optimization of partially defined functions under unknown constraints ⋮ Efficient hierarchical surrogate-assisted differential evolution for high-dimensional expensive optimization ⋮ Handling of Constraints in Efficient Global Optimization ⋮ Analysis of multi-objective Kriging-based methods for constrained global optimization

Uses Software

• GENESIS
• SPACE
• EGO
• ZAERO

Cites Work

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• A sampling-based approach for probabilistic design with random fields
• Kriging metamodeling in simulation: a review
• Efficient global optimization of expensive black-box functions
• Interpolation of spatial data. Some theory for kriging
• The design and analysis of computer experiments.
• A note on Platt's probabilistic outputs for support vector machines
• A convex hull approach for the reliability-based design optimization of nonlinear transient dynamic problems
• The origins of kriging

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