A Lagrangian augmented Hopfield network for mixed integer non-linear programming problems

The augmented Lagrangian function has better convergence and solution properties for mixed integer non-linear programming problems than either standard Lagrangian function, or penalty function approaches. The Hopfield neural network based approaches that have been proposed to solve this function hav...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 42; H. 1; S. 323 - 330
Hauptverfasser: Dillon, Joseph D., O'Malley, Mark J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 2002
Schlagworte:
Augmented Lagrangian
Hopfield neural network
Mixed integer non-linear programming
Mixed integer non-linear programming
Augmented Lagrangian
Hopfield neural network
ISSN:0925-2312, 1872-8286
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Zusammenfassung:The augmented Lagrangian function has better convergence and solution properties for mixed integer non-linear programming problems than either standard Lagrangian function, or penalty function approaches. The Hopfield neural network based approaches that have been proposed to solve this function have used continuous neurons to represent discrete variables. The augmented Hopfield network has both discrete and continuous neurons. Here a Lagrangian augmented Hopfield network (LAHN) is constructed by including augmented Lagrangian multiplier neurons in the augmented Hopfield network. This new network is applied to the generator scheduling problem (a mixed integer non-linear programming problem) and results illustrate that improved solutions are obtained.
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(01)00585-9