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.
- Authors:
-
[1]
; [2]
- Saint Joseph's Univ., Philadelphia, PA (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Publication Date:
- Grant/Contract Number:
- AC02-06CH11357
- Type:
- Accepted Manuscript
- Journal Name:
- Optimization Methods and Software
- Additional Journal Information:
- Journal Volume: 32; Journal Issue: 3; Journal ID: ISSN 1055-6788
- Publisher:
- 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
- Language:
- English
- Subject:
- 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:
- 1373699
Regis, Rommel G., and Wild, Stefan M.. CONORBIT: constrained optimization by radial basis function interpolation in trust regions. United States: N. p.,
Web. doi:10.1080/10556788.2016.1226305.
Regis, Rommel G., & Wild, Stefan M.. CONORBIT: constrained optimization by radial basis function interpolation in trust regions. United States. doi:10.1080/10556788.2016.1226305.
Regis, Rommel G., and Wild, Stefan M.. 2016.
"CONORBIT: constrained optimization by radial basis function interpolation in trust regions". United States.
doi:10.1080/10556788.2016.1226305. https://www.osti.gov/servlets/purl/1373699.
@article{osti_1373699,
title = {CONORBIT: constrained optimization by radial basis function interpolation in trust regions},
author = {Regis, Rommel G. and Wild, Stefan M.},
abstractNote = {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.},
doi = {10.1080/10556788.2016.1226305},
journal = {Optimization Methods and Software},
number = 3,
volume = 32,
place = {United States},
year = {2016},
month = {9}
}
- Free Publicly Accessible Full Text
-
Accepted Manuscript (DOE)
- Publisher's Version of Record
- https://doi.org/10.1080/10556788.2016.1226305