A hierarchical approach for solving the large-scale traveling salesman problem

Authors

Anthony L. Figueras, Florida International University

Document Type

Thesis

Degree

Master of Science (MS)

Major/Program

Computer Engineering

First Advisor's Name

Dong C. Park

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Wunnava V. Subbarao

Third Advisor's Name

Farah Arefi

Fourth Advisor's Name

Malek Adjouadi

Keywords

Traveling-salesman problem, Neural networks (Computer science)

Date of Defense

4-6-1994

Abstract

An algorithm for solving the large-scale Traveling Salesman Problem is presented. Research into past work in the area of Hopfield neural network use in solving the Traveling Salesman Problem has yielded design ideas that have been incorporated into this work. The algorithm consists of an unsupervised learning algorithm and a recursive Hopfield neural network. The unsupervised learning algorithm was used to decompose the problem into clusters. The recursive Hopfield neural network was applied to the centroids of the clusters, then to the cities in each cluster, in order to find an optimal path. An improvement in both computation speed and solution accuracy is shown by the proposed algorithm over the straight use of the Hopfield neural network.

Identifier

FI15101396

Recommended Citation

Figueras, Anthony L., "A hierarchical approach for solving the large-scale traveling salesman problem" (1994). FIU Electronic Theses and Dissertations. 3321.
https://digitalcommons.fiu.edu/etd/3321