Title :
A hierarchical approach for solving large-scale traveling salesman problem
Author :
Park, Dong C. ; Figueras, Anthony L. ; Chen, Carl
Author_Institution :
Intelligent Comput. Res. Lab., Florida Int. Univ., Miami, FL, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
A hierarchical approach which combines an unsupervised learning algorithm and a recursive Hopfield neural network is proposed for solving the large-scale traveling salesman problem (TSP). For a TSP with given number of cities, an unsupervised learning algorithm was first used to find the clusters for decomposing the given problem and then a recursive version of the Hopfield network was applied to the centroids of the clusters. The proposed recursive Hopfield network was also applied to cities in each cluster in order to find an optimal path. A final tour was obtained by connecting together the resultant paths of each cluster
Keywords :
Hopfield neural nets; operations research; optimisation; travelling salesman problems; unsupervised learning; centroids; clusters; hierarchical approach; large-scale problems; operations research; optimal path; recursive Hopfield neural network; traveling salesman problem; unsupervised learning; Annealing; Cities and towns; Clustering algorithms; Electronic mail; Intelligent networks; Large-scale systems; Neurons; Temperature; Traveling salesman problems; Unsupervised learning;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.375019
