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

Meeting Name

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

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

ART Neural Nets; ART Neural Network; Adaptive Resonance Theory; Approximate Solutions; Approximation Theory; Mathematics Computing; Self Organizing Feature Maps; Self-Organising Feature Maps; Travelling Salesman Problems

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2001 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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