CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Performance Comparison of PSO and GA Applied to TSP

by Abdelhakim Gharib, Jamal Benhra, Mohsine Chaouqi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 15
Year of Publication: 2015
Authors: Abdelhakim Gharib, Jamal Benhra, Mohsine Chaouqi
10.5120/ijca2015907188

Abdelhakim Gharib, Jamal Benhra, Mohsine Chaouqi . A Performance Comparison of PSO and GA Applied to TSP. International Journal of Computer Applications. 130, 15 ( November 2015), 34-39. DOI=10.5120/ijca2015907188

@article{ 10.5120/ijca2015907188,
author = { Abdelhakim Gharib, Jamal Benhra, Mohsine Chaouqi },
title = { A Performance Comparison of PSO and GA Applied to TSP },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 15 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number15/23288-2015907188/ },
doi = { 10.5120/ijca2015907188 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:25:58.186399+05:30
%A Abdelhakim Gharib
%A Jamal Benhra
%A Mohsine Chaouqi
%T A Performance Comparison of PSO and GA Applied to TSP
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 15
%P 34-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of this article is to present a collective intelligence approach to help solving optimization problems and apply it in particular to the Travelling Salesman Problem. The approach used is the particle swarm optimization (PSO) whose main idea is to simulate the collective behavior of a cloud. This article also compares the results obtained using PSO algorithm with those obtained by using another famous metaheuristic wich is the Genetic Algorithm.

References
  1. Aardal, K., Hoesel, S. v., Lenstra, J. K. and Stougie, L. (1997). A Decade of Combinatorial Optimization. Department of Information and Computing Sciences, Utrecht University, UU-CS-1997-12.
  2. Festa, P. and Resende, M. G. C. (2008). Hybrid Grasp Heuristics. AT&T Labs Research, Florham Park, July.
  3. El Hassani Hicham, Said Benkachcha, Jamal Benhra, “New genetic operator (jump crossover) for the traveling salesman problem”. International Journal of Applied Metaheuristic Computing, 6(2), 33-44, April-June 2015 33.
  4. A. H. Sabry, A. Bacha, and J. Benhra, "A contribution to solving the traveling salesman problem using ant colony optimization and web mapping platforms Application to logistics in a urban context," in Codit'14, Metz, France, 2014.
  5. K. Menger, Ergebnisse eines mathematischen Kolloquiums: Deuticke, 1932Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  6. A. Punnen, "The Traveling Salesman Problem: Applications, Formulations and Variations," in The Traveling Salesman Problem and Its Variations. vol. 12, G. Gutin and A. Punnen, Eds., ed: Springer US, 2007, pp. 1-28.
  7. C. H. Papadimitriou, "The Euclidean travelling salesman problem is NP-complete," Theoretical Computer Science, vol. 4, pp. 237-244, 1977.
  8. Goldbarg, E.F.G; Souza, G.R. & Goldbarg, M.C. (2006a). Particle swarm for the traveling salesman problem, Proceedings of the EvoCOP 2006, Gottlieb, J. & Raidl, G.R. (Ed.), Lecture Notes in Computer Science, Vol. 3906, pp. 99-110, Budapest, Hungary, April 2006, Springer, Berlin.
  9. Kennedy, J. and Eberhart, R. (1995). Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks, pp. 19421948, 27th November – 1 December, 1995.
  10. J. H. Holland, "Adaption in Natural and Artificial Systems," The University of Michigan Press, 1975.
  11. D.E. Goldberg. "Genetic Algorithms in Search, Optimization, and Machine Learning". AddisonWesley, New York, 1989.
  12. Kemighan, L. S. (1973). "An Effective Heuristic Algorithm for the Traveling Salesman Problem." Opérations Research 21: 2245-2269.
  13. G. Reinelt, "TSPLIB—A Traveling Salesman Problem Library," ORSA Journal of Computing, vol. 3, pp. 376-384, 1991.
  14. Shi XH, Liang YC, Lee HP, Lu C, Wang QX, “Particle swarm optimization-based algorithms for TSP and generalized TSP”, Inf Process Lett 103(5), 2007, pp.169–176.
Index Terms

Computer Science
Information Sciences

Keywords

Traveling salesman problem Particle Swarm Optimization Genetic algorithm

Powered by PhDFocusTM