Metamodeling for large-scale optimization tasks based on object networks

Optimization in large-scale networks - such as large logistical networks and electric power grids involving many thousands of variables - is a very challenging task. In this paper, we present the theoretical basis and the related experiments involving the development and use of visualization tools a...

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Bibliographic Details
Published inThe 2011 International Joint Conference on Neural Networks pp. 2905 - 2910
Main Authors Werbos, L., Kozma, R., Silva-Lugo, R., Pazienza, G. E., Werbos, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2011
Subjects
Data visualization
Linear programming
Logistics
Metamodeling
Neural networks
Optimization
Software
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Summary:Optimization in large-scale networks - such as large logistical networks and electric power grids involving many thousands of variables - is a very challenging task. In this paper, we present the theoretical basis and the related experiments involving the development and use of visualization tools and improvements in existing best practices in managing optimization software, as preparation for the use of "metamodeling" - the insertion of complex neural networks or other universal nonlinear function approximators into key parts of these complicated and expensive computations; this novel approach has been developed by the new Center for Large-Scale Integrated Optimization and Networks (CLION) at University of Memphis, TN.
ISBN:1424496357
9781424496358
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2011.6033602