We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

A New Hybrid Algorithm for Traveler Salesman Problem based on Genetic Algorithms and Artificial Neural Networks

by Alireza Arab Asadi, Ali Naserasadi, Zeinab Arab Asadi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 24 - Number 5
Year of Publication: 2011
Authors: Alireza Arab Asadi, Ali Naserasadi, Zeinab Arab Asadi
10.5120/2945-3926

Alireza Arab Asadi, Ali Naserasadi, Zeinab Arab Asadi . A New Hybrid Algorithm for Traveler Salesman Problem based on Genetic Algorithms and Artificial Neural Networks. International Journal of Computer Applications. 24, 5 ( June 2011), 6-9. DOI=10.5120/2945-3926

@article{ 10.5120/2945-3926,
author = { Alireza Arab Asadi, Ali Naserasadi, Zeinab Arab Asadi },
title = { A New Hybrid Algorithm for Traveler Salesman Problem based on Genetic Algorithms and Artificial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 24 },
number = { 5 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume24/number5/2945-3926/ },
doi = { 10.5120/2945-3926 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:09.278126+05:30
%A Alireza Arab Asadi
%A Ali Naserasadi
%A Zeinab Arab Asadi
%T A New Hybrid Algorithm for Traveler Salesman Problem based on Genetic Algorithms and Artificial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 24
%N 5
%P 6-9
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Traveler Salesman Problem (TSP) is one the most famous and important problems in the field of operation research and optimization. This problem is a NP-Hard problem and it is aimed to find a minimum Hamiltonian cycle in a connected and weighed graph. In the last decades, many innovative algorithms have been presented to solve this problem but most of them are inappropriate and inefficient and have high complexity. In this paper, we combined Hopfield neural network with genetic algorithm to solve this problem, and showed that the results of the algorithm are more efficient that the other similar algorithms.

References
  1. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, 3rd edition, MIT press, 2010.
  2. C. Neapolitoan and K. Naimipour, Foundations of Algorithms Using C++ Pseudocode, Jones and Bartlett Publishers, 2004.
  3. B. Freisieben and P. Merz, New Genetic Local Search Operator for Travelling salesman Problem, Conference on Parallel Problem Solving From nature, app. 890-899, 1996.
  4. P. van Laarhoven and E. H. L. Aarts, Simulated Annealing: Theory and Applications, KluwerAcademic, 1987,
  5. M. Dorigo and L. M. Gambradella, Ant Colony System: A Cooperative Learning Approach to theTravelling Salesman Problem, JEEE Transaction on Evolutionary Computation, Vol. l, pp. 53-66, 1997.
  6. ID.E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley, 1989.
  7. Naef Taher Al Rahedi and Jalal Atoum, Solving TSP problem using New Operator in GeneticAlgorithms, American Journal of Applied Sciences 6(8):1586-1590, 2009.
  8. S. R. Hejazi, R. Soltani, Hybrid Algorithm for TSP based on Ant Colony and Genetic Algorithm, 4th conference on industries, 2006.
  9. Gang Feng and Christos Douligeris, Using Hopfield networks to solve traveling salesman problems based on stable state analysis technique, Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference.
  10. S.Ghafurian and N. Javadian, Ant Colony Algorithm for Solving fixed destination multi-depot multiple travelling salesman problems, Applied Soft Computing, Vol. 11, Issue 1, January 2011, pp. 1256 – 1262.
  11. D. Kriesel, a Brief Introduction to Neural Networks, http://www.dkiesel.com/en/science/neural_network, 2011.
  12. S. J. Russell and P.Norvig, Artificial Intelligence: A Modern Approach, 3rd edition, Pearson education Inc, 2010.
  13. Banzhaf, Wolfgang; Nordin, Peter; Keller, Robert; Francone, Frank (1998) Genetic Programming – An Introduction, Morgan Kaufmann, San Francisco, CA..
  14. Bies, Robert R; Muldoon, Matthew F; Pollock, Bruce G; Manuck, Steven; Smith, Gwenn and Sale, Mark E (2006). "A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection". Journal of Pharmacokinetics and Pharmacodynamics (Netherlands: Springer): 196–221.
  15. Mitchell, Melanie, (1996), An Introduction to Genetic Algorithms, MIT Press, Cambridge, MA.
  16. Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing 4, 65–85.
  17. Hingston,Philip F.; Barone, Luigi C.; Michalewicz, Zbigniew (2008) Design by Evolution: Advances in Evolutionary Design:297
  18. Eiben,Agoston E.; Smith, James E. (2003) Introduction to Evolutionary Computing
  19. A. Solomon and B. W. Colleti, a Quasibelian landscape of the travelling salesman problem are elementary, Discrete Optimization, Vol. 6, Issue 3, Aug. 2003, pp. 288-291.
Index Terms

Computer Science
Information Sciences

Keywords

Travelers Salesman Problem Genetic Algorithm Hopfield Neural Network NP-Hard Problem