A neural network approach to multiobjective and multilevel programming problems

This study aims at utilizing the dynamic behavior of artificial neural networks (ANNs) to solve multiobjective programming (MOP) and multilevel programming (MLP) problems. The traditional and non-traditional approaches to the MLP are first classified into five categories. Then, based on the approach...

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Bibliographic Details
Published in:Computers & mathematics with applications (1987) 2004-07, Vol.48 (1), p.95-108
Main Authors: Shih, Hsu-Shih, Wen, Ue-Pyng, Lee, S., Lan, Kuen-Ming, Hsiao, Han-Chyi
Format: Article
Language:English
Subjects:
Dynamic behavior
Energy function
Multilevel programming
Multiobjective programming
Neural network
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Summary:This study aims at utilizing the dynamic behavior of artificial neural networks (ANNs) to solve multiobjective programming (MOP) and multilevel programming (MLP) problems. The traditional and non-traditional approaches to the MLP are first classified into five categories. Then, based on the approach proposed by Hopfield and Tank [1], the optimization problem is converted into a system of nonlinear differential equations through the use of an energy function and Lagrange multipliers. Finally, the procedure is extended to MOP and MLP problems. To solve the resulting differential equations, a steepest descent search technique is used. This proposed nontraditional algorithm is efficient for solving complex problems, and is especially useful for implementation on a large-scale VLSI, in which the MOP and MLP problems can be solved on a real time basis. To illustrate the approach, several numerical examples are solved and compared.
ISSN:0898-1221
1873-7668
DOI:10.1016/j.camwa.2003.12.003