Tuning BARON using derivative-free optimization algorithms

Optimization solvers include many options that allow users to control algorithmic aspects that may have a considerable impact on solver performance. Tuning solver options is often necessary to reduce execution time and improve solution quality. Previous studies of solver tuning techniques have focus...

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Vydané v:Journal of global optimization Ročník 74; číslo 4; s. 611 - 637
Hlavní autori: Liu, Jianfeng, Ploskas, Nikolaos, Sahinidis, Nikolaos V.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 15.08.2019
Springer
Springer Nature B.V
Predmet:
Algorithms
Analysis
Computer Science
Global optimization
Integer programming
Linear programming
Mathematical optimization
Mathematics
Mathematics and Statistics
Nonlinear programming
Operations Research/Decision Theory
Optimization
Performance enhancement
Real Functions
Solvers
Tuning
Solver tuning
Global optimization
Derivative-free optimization
ISSN:0925-5001, 1573-2916
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Shrnutí:Optimization solvers include many options that allow users to control algorithmic aspects that may have a considerable impact on solver performance. Tuning solver options is often necessary to reduce execution time and improve solution quality. Previous studies of solver tuning techniques have focused on mixed-integer linear programming and local nonlinear programming solvers. In this paper, we investigate the potential of tuning a global optimization solver for nonlinear and mixed-integer nonlinear programming problems. In particular, derivative-free optimization (DFO) algorithms are used to find optimal values for options of the global optimization solver BARON. A set of 126 problems from the GLOBALLib and MINLPLib collections are utilized in a computational study from which we conclude that tuning options can improve the default performance of BARON for individual problems and an entire library. Additionally, we present a systematic comparison of 27 DFO solvers in terms of their ability to improve the performance of the global solver. We find that several DFO implementations are much better than others in terms of finding optimal tuning parameters.
Bibliografia:ObjectType-Article-1
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-018-0640-3