A Python/C library for bound-constrained global optimization with continuous GRASP

被引:0
|
作者
R. M. A. Silva
M. G. C. Resende
P. M. Pardalos
M. J. Hirsch
机构
[1] Federal University of Pernambuco,Centro de Informática (CIn)
[2] AT&T Labs Research,Algorithms and Optimization Research Department
[3] University of Florida,Department of Industrial and Systems Engineering
[4] Intelligence and Information Systems,Raytheon Company
来源
Optimization Letters | 2013年 / 7卷
关键词
GRASP; Continuous GRASP; Global optimization; Multimodal functions; Continuous optimization; Heuristic; Stochastic algorithm; Stochastic local search; Nonlinear programming;
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摘要
This paper describes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\texttt{libcgrpp}}$$\end{document} , a GNU-style dynamic shared Python/C library of the continuous greedy randomized adaptive search procedure (C-GRASP) for bound constrained global optimization. C-GRASP is an extension of the GRASP metaheuristic (Feo and Resende, 1989) and has been used to solve unstable and nondifferentiable problems, as well as hard global optimization problems, such as chemical equilibrium systems and robot kinematics applications (Hirsch et al. in Optim lett 1:201–212, 2007). After a brief introduction to C-GRASP, we show how to download, install, configure, and use the library through an illustrative example.
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页码:967 / 984
页数:17
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