Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions

• Published in: Computers & operations research Vol. 38; no. 5; pp. 837 - 853
• Main Author: Regis, Rommel G.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.05.2011; Elsevier; Pergamon Press Inc
• Subjects: Algorithms; Applied sciences; Constrained optimization; Construction; Decision theory. Utility theory; Derivative-free optimization; Exact sciences and technology; Expensive function; Genetic algorithms; Handles; Inequalities; Large-scale optimization; Linear inference, regression; Mathematical analysis; Mathematical models; Mathematical programming; Mathematics; Operational research and scientific management; Operational research. Management science; Optimization; Optimization algorithms; Probability and statistics; Quadratic programming; Radial basis function; Sciences and techniques of general use; Statistics; Stochastic algorithm; Studies; Surrogate model; Radial basis function; Surrogate model; Expensive function; Stochastic algorithm; Large-scale optimization; Constrained optimization; Derivative-free optimization; Black box; Modeling; Adaptive method; Function evaluation; Sequential method; Inequality constraint; Mesh method; Large scale system; Decision making; Model selection; Objective analysis; Quadratic programming; Kriging; Search algorithm; Genetic algorithm; Automobile industry; Alternative method; Metric; Large scale; Objective function
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2010.09.013

This paper presents a new algorithm for derivative-free optimization of expensive black-box objective functions subject to expensive black-box inequality constraints. The proposed algorithm, called ConstrLMSRBF, uses radial basis function (RBF) surrogate models and is an extension of the Local Metri...