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Title: CONORBIT: constrained optimization by radial basis function interpolation in trust regions

Here, this paper presents CONORBIT (CONstrained Optimization by Radial Basis function Interpolation in Trust regions), a derivative-free algorithm for constrained black-box optimization where the objective and constraint functions are computationally expensive. CONORBIT employs a trust-region framework that uses interpolating radial basis function (RBF) models for the objective and constraint functions, and is an extension of the ORBIT algorithm. It uses a small margin for the RBF constraint models to facilitate the generation of feasible iterates, and extensive numerical tests confirm that such a margin is helpful in improving performance. CONORBIT is compared with other algorithms on 27 test problems, a chemical process optimization problem, and an automotive application. Numerical results show that CONORBIT performs better than COBYLA, a sequential penalty derivative-free method, an augmented Lagrangian method, a direct search method, and another RBF-based algorithm on the test problems and on the automotive application.
 [1] ;  [2]
  1. Saint Joseph's Univ., Philadelphia, PA (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Optimization Methods and Software
Additional Journal Information:
Journal Volume: 32; Journal Issue: 3; Journal ID: ISSN 1055-6788
Taylor & Francis
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; black-box optimization; computationally expensive function; constrained optimization; fully linear model; radial basis function interpolation; simulation-based optimization; trust region
OSTI Identifier: