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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/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

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