A generalized model of statistical Hopfield neural network to solve TSP

Author

Kim, Yoo-Shin ; Cha, Seon-geun ; Kim, Ja-myeong

Author_Institution

Pusan Nat. Univ., South Korea

fYear

1997

fDate

9-12 Sep 1997

Firstpage

693

Abstract

The travelling salesman problem (TSP) is the well-known NP-complete problem and many researchers have tried to solve it, but no method can solve this problem completely. One of the most dominant method is the statistical Hopfield (1982, 1984) neural network. We propose a generalized model which overcomes the weak points of Van Den Bout (1989) and Park´s (1991) method. We improve the energy function and consider all kinds of perturbation effects and the ratio of these effects. Through a simulation for a randomly generated distribution of 10 city, we found that our proposed model shows that 90 out of 100 cases reach the optimum and a near optimum solution within a 5% error

Keywords

Hopfield neural nets; random processes; simulated annealing; statistical analysis; travelling salesman problems; NP-complete problem; TSP; critical temperature; energy function; generalized model; optimum solution; perturbation effects; randomly generated distribution; ratio; simulated annealing; simulation; statistical Hopfield neural network; travelling salesman problem; Boltzmann equation; Cities and towns; Cooling; Hopfield neural networks; NP-complete problem; Neural networks; Polynomials; Simulated annealing; Temperature;

fLanguage

English

Publisher

ieee

Conference_Titel

Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on

Print_ISBN

0-7803-3676-3

Type

conf

DOI

10.1109/ICICS.1997.652066

Filename

652066