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 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. (C) 2002 Elsevier Science B.V. All rights reserved.