By Topic

Using Hopfield networks to solve traveling salesman problems based on stable state analysis technique

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Feng, G. ; Dept. of Electr. & Comput. Eng., Miami Univ., FL, USA ; Douligeris, C.

We have elsewhere developed a general method called the stable state analysis technique to determine constraints that the weights in the Hopfield energy function must satisfy so that valid solutions of high quality can be always obtained. In this paper, the effectiveness of this method is demonstrated through a reinvestigation of the capability of the Hopfield neural net (HNN) to solve the traveling salesman problem (TSP). A large number of experiments on 10-city TSPs demonstrate the proposed method can obtain results comparable to those obtained using simulated annealing, while the mean error of achieved solutions to a 51-city TSP is about 15% longer than the optimal tour, which is much better than that of solutions obtained through other HNN-based methods

Published in:

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on  (Volume:6 )

Date of Conference:

2000