ART/SOFM: a hybrid approach to the TSP

Author

Vishwanathan, Narayan ; Wunsch, Donald C., II

Author_Institution

Dept. of Electr. & Comput. Eng., Missouri Univ., Rolla, MO, USA

Volume

4

fYear

2001

fDate

2001

Firstpage

2554

Abstract

We present a new method of solving large scale travelling salesman problem (TSP) instances using a combination of adaptive resonance theory (ART) and self organizing feature maps (SOFM). We divide our algorithm into three phases: phase one uses ART to form clusters of cities; phase two uses a novel modification of the traditional SOFM algorithm to solve a slight variant of the TSP in each cluster of cities; and phase three uses another version of the SOFM to link all the clusters. The experimental results show that our algorithm finds approximate solutions which are about 13% longer than those reported by the chained Lin Kernighan method for problem sizes of 14,000 cities

Keywords

ART neural nets; approximation theory; mathematics computing; self-organising feature maps; travelling salesman problems; ART neural network; adaptive resonance theory; approximate solutions; self organizing feature maps; travelling salesman problem; Cities and towns; Clustering algorithms; Computational intelligence; Hopfield neural networks; Laboratories; Neural networks; Neurons; Resonance; Subspace constraints; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.938771

Filename

938771