A hybrid neural network model for solving optimization problems

Author

Sun, K.T. ; Fu, H.C.

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Chiao-Tung Univ., Hsinchu, Taiwan

Volume

42

Issue

2

fYear

1993

fDate

2/1/1993 12:00:00 AM

Firstpage

218

Lastpage

227

Abstract

A hybrid neural network model for solving optimization problems is proposed. An energy function which contains the constraints and cost criteria of an optimization problem is derived, and then the neural network is used to find the global minimum (or maximum) of the energy function, which corresponds to a solution of the optimization problem. The network contains two subnets: a constraint network and a goal network. The constraint network models the constraints of an optimization problem and computes the gradient (updating) value of each neuron such that the energy function monotonically converges to satisfy all constraints of the problem. The goal network points out the direction of convergence for finding an optimal value for the cost criteria. These two subnets ensure that the neural network finds feasible as well as optimal (or near-optimal) solutions. The traveling salesman problem and the Hamiltonian cycle problem are used to demonstrate the method

Keywords

constraint handling; neural nets; operations research; optimisation; Hamiltonian cycle problem; constraints; cost criteria; energy function; global minimum; goal network; hybrid neural network model; optimization problems; traveling salesman problem; Computer networks; Constraint optimization; Cost function; Equations; Hopfield neural networks; Neural networks; Optimization methods; Sun; Testing; Traveling salesman problems;

fLanguage

English

Journal_Title

Computers, IEEE Transactions on

Publisher

ieee

ISSN

0018-9340

Type

jour

DOI

10.1109/12.204794

Filename

204794