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

Abstract

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]
  1. Saint Joseph's Univ., Philadelphia, PA (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1373699
Grant/Contract Number:
AC02-06CH11357
Resource Type:
Journal Article: 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
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

Citation Formats

Regis, Rommel G., and Wild, Stefan M. CONORBIT: constrained optimization by radial basis function interpolation in trust regions. United States: N. p., 2016. 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. Mon . "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 = {Mon Sep 26 00:00:00 EDT 2016},
month = {Mon Sep 26 00:00:00 EDT 2016}
}

Journal Article:
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