Sequential approximate optimization using radial basis function network for engineering optimization

DOI10.1007/s11081-010-9118-yzbMath1284.65077OpenAlexW2030757653MaRDI QIDQ2443397

Satoshi Kitayama, Koetsu Yamazaki, Masao Arakawa

Publication date: 7 April 2014

Published in: Optimization and Engineering (Search for Journal in Brave)

Full work available at URL: https://kanazawa-u.repo.nii.ac.jp/?action=repository_uri&item_id=8499

zbMATH Keywords

density functionresponse surfacesequential approximate optimizationengineering optimizationRBF network

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (9)

Optimization of the complex-RFM optimization algorithm ⋮ Mesh-based Nelder-Mead algorithm for inequality constrained optimization ⋮ A novel sequential approximate optimization approach using data mining for engineering design optimization ⋮ Quantifying uncertainty with ensembles of surrogates for blackbox optimization ⋮ Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates ⋮ Order-based error for managing ensembles of surrogates in mesh adaptive direct search ⋮ The Mesh Adaptive Direct Search Algorithm for Granular and Discrete Variables ⋮ Combining cross-entropy and MADS methods for inequality constrained global optimization ⋮ Sequential approximate optimization for discrete design variable problems using radial basis function network

Uses Software

• EGO

Cites Work

• Unnamed Item
• On the design of optimization strategies based on global response surface approximation models
• Efficient global optimization of expensive black-box functions
• Crashworthiness design optimization using successive response surface approximations
• Metamodeling: Radial basis functions, versus polynomials
• The equivalence of support vector machine and regularization neural networks
• Simulation-based optimization using computational intelligence
• Global and local optimization using radial basis function response surface models
• Global optimization of stochastic black-box systems via sequential kriging meta-models
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Metamodels for computer-based engineering design: Survey and recommendations

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