A surrogate-based optimization method with RBF neural network enhanced by linear interpolation and hybrid infill strategy

DOI10.1080/10556788.2013.777722zbMath1285.90072OpenAlexW2070356494MaRDI QIDQ5746706

No author found.

Publication date: 7 February 2014

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2013.777722

zbMATH Keywords

sequential samplinglinear interpolationRBF neural networksurrogate-based optimizationinfill strategywidth optimization

Mathematics Subject Classification ID

Nonlinear programming (90C30) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (6)

ARGONAUT: algorithms for global optimization of constrained grey-box computational problems ⋮ Sequential approximate optimization with adaptive parallel infill strategy assisted by inaccurate Pareto front ⋮ A novel sequential approximate optimization approach using data mining for engineering design optimization ⋮ Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO ⋮ Surrogate-based distributed optimisation for expensive black-box functions ⋮ Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption

Cites Work

• Unnamed Item
• Expected improvement in efficient global optimization through bootstrapped Kriging
• Constrained global optimization of expensive black box functions using radial basis functions
• Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions
• A method for simulation based optimization using radial basis functions
• An informational approach to the global optimization of expensive-to-evaluate functions
• A response-modeling approach to global optimization and OUU
• Improved strategies for radial basis function methods for global optimization
• An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
• An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization
• Global optimization of expensive-to-evaluate functions: An empirical comparison of two sampling criteria
• Efficient global optimization of expensive black-box functions
• Application of Bayesian approach to numerical methods of global and stochastic optimization
• 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 sequential-design metamodeling strategy for simulation optimization
• Global optimization of stochastic black-box systems via sequential kriging meta-models
• A radial basis function method for global optimization

This page was built for publication: A surrogate-based optimization method with RBF neural network enhanced by linear interpolation and hybrid infill strategy