An Artificial Neural Network Approach to Multi-objective Programming and Multi-level Programming Problems

機構典藏 > College of Business and Management > Department of Management Sciences > Journal Article > Item 987654321/100422

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/100422

Title:	An Artificial Neural Network Approach to Multi-objective Programming and Multi-level Programming Problems
Authors:	Shih, Hsu-Shih;Wen, Ue-Pyng;Lee, S.;Lan, Kuen-Ming;Hsiao, Han-Chyi
Contributors:	淡江大學管理科學學系
Keywords:	Neural network;Energy function;Multilevel programming;Multiobjective programming;Dynamic behavior
Date:	2004-07
Issue Date:	2015-02-27 13:58:45 (UTC+8)
Publisher:	Kidlington: Pergamon Press
Abstract:	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.
Relation:	Computers & Mathematics with Applications 48(1–2), pp.95–108
Appears in Collections:	[Department of Management Sciences] Journal Article

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	323	View/Open

Loading...