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Implementation of Different Ant based Techniques for Network Load Analysis

by Nidhi Nayak, Bhupesh Gour, Asif Ullah Khan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 9
Year of Publication: 2013
Authors: Nidhi Nayak, Bhupesh Gour, Asif Ullah Khan
10.5120/13422-1101

Nidhi Nayak, Bhupesh Gour, Asif Ullah Khan . Implementation of Different Ant based Techniques for Network Load Analysis. International Journal of Computer Applications. 77, 9 ( September 2013), 20-24. DOI=10.5120/13422-1101

@article{ 10.5120/13422-1101,
author = { Nidhi Nayak, Bhupesh Gour, Asif Ullah Khan },
title = { Implementation of Different Ant based Techniques for Network Load Analysis },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 9 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number9/13422-1101/ },
doi = { 10.5120/13422-1101 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:49:49.453551+05:30
%A Nidhi Nayak
%A Bhupesh Gour
%A Asif Ullah Khan
%T Implementation of Different Ant based Techniques for Network Load Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 9
%P 20-24
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Network Load balancing is a technique of balancing at each node the number of packets received and the number of packets forward to the other node so that the chance of network congestion problem has been reduced and bandwidth is utilized. Although there are many techniques implemented for the balancing of nodes based on maintaining a routing table at each node and is updated as the packet get forward from that node. Ant Colony Optimization is one of the techniques used in the network for the balancing of number of packets at each node. Here in this paper is proposed a comparative study of different ant colony optimization techniques implemented for the analysis of the network load balancing. Here the ant based techniques are implemented are simulated for different conditions and on the basis of which proposed the best ant based techniques for the network load balancing.

References
  1. Le Lu, Shanguo Huang, Wanyi Gu",A Dynamic Ant Colony Optimization for Load Balancing in MRN/MLN", SPIE Vol. 8310, 831010 . SPIE-OSA IEEE . CCC code: 0277-786X/11/$18. doi: 10. 1117/12. 904391 SPIE-OSA IEEE/Voi. 8310 831010-1 100876 China, © 2011.
  2. . M. Dorigo, Ant colony optimization web page, http://iridia. ulb. ac. be/mdorigo/ACO/ACO. html
  3. M. Dorigo, G. Di Caro & L. M. Gambardella. Ant Algorithms for Discrete Optimization. Artificial Life, 5(2):137-172. 1999.
  4. A. Colorni, M. Dorigo, and V. Maniezzo, Distributed optimization by ant colonies, Proceedings of ECAL'91, European Conference on Artificial Life, Elsevier Publishing, Amsterdam, 1991.
  5. M. Dorigo, V. Maniezzo, and A. Colorni, The ant system: an autocatalytic optimizing process, Technical Report TR91-016, Politecnico di Milano ,1991.
  6. M. Dorigo, Optimization, learning and natural algorithms Ph. D. Thesis, Politecnico di Milano, Milano, 1992.
  7. M. Dorigo, T. Stützle. The ant colony optimization metaheuristic: Algorithms, applications and advances. In F. Glover and G. Kochenberger, editors, Handbook of Metaheuristics. Kluwer Academic Publishers, To appear in 2002.
  8. En-Jui Chang, Chih-Hao Chao, Kai-Yuan Jheng, Hsien-Kai Hsin, and An-Yeu Wu "ACO-Based Cascaded Adaptive Routing for Traffic Balancing in NoC Systems" Graduate Institute of Electronics Engineering, National Taiwan University, Taipei 106, Taiwan
  9. Ruud Schoonderwoerd1,Owen Holland and Janet Bruten," Ant-like agents for load balancing in telecommunications networks", Agents'97 Marina del Rey CA USA ACM 1997.
  10. Gambardella & Dorigo ,"Ant colonies for the traveling salesman problem" Accepted for publication in BioSystems, TR/IRIDIA/1996-3 Université Libre de Bruxelles Belgium 1997.
  11. Luca M. Gambardella, Marco Dorigo "Ant-Q: A Reinforcement Learning approach to the traveling salesman problem. "
  12. S. Sanyal, R. S, and S. Biswas. Necessary and su?cient conditions for success of the metropolis algorithm for optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '10), page 1417–1424. ACM, 2010.
  13. F. Neumann and C. Witt. Runtime analysis of a simple ant colony optimization algorithm. In Proceedings of the 17th International Symposium on Algorithms and Computation (ISAAC '06), volume 4288 of LNCS, pages 618–627. Springer, 2006.
  14. B. Doerr, F. Neumann, D. Sudholt, and C. Witt. On the runtime analysis of the 1-ANT ACO algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '07), pages 33–40. ACM, 2007.
  15. B. Doerr and D. Johannsen. Re?ned runtime analysis of a basic ant colony optimization algorithm. In Proceedings of the Congress of Evolutionary Computation (CEC '07), pages 501–507. IEEE Press, 2007.
  16. W. J. Gutjahr and G. Sebastiani. Runtime analysis of ant colony optimization with best-so-far reinforcement. Methodology and Computing in Applied Probability, 10: 409–433, 2008.
  17. F. Neumann, D. Sudholt, and C. Witt. Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intelligence, 3(1):35–68, 2009.
  18. F. Neumann, D. Sudholt, and C. Witt. Rigorous analyses for the combination of ant colony optimization and local search. In Proceedings of the Sixth International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS '08), volume 5217 of LNCS, pages 132–143. Springer, 2008.
  19. F. Neumann and C. Witt. Ant Colony Optimization and the minimum spanning tree problem. In Proceedings of Learning and Intelligent Optimization (LION '07), volume 5313 of LNCS, pages 153–166. Springer, 2008.
  20. T. Kotzing, P. K. Lehre, P. S. Oliveto, and F. Neumann. ¨ Ant colony optimization and the minimum cut problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '10), pages 1393–1400. ACM, 2010.
  21. Y. Zhou. Runtime analysis of an ant colony optimization algorithm for TSP instances. IEEE Transactions on Evolutionary Computation, 13(5):1083–1092, 2009.
  22. T. Kotzing, F. Neumann, H. R ¨ oglin, and C. Witt. The- ¨ oretical properties of two ACO approaches for the traveling salesman problem. In Seventh International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS '10), volume 6234 of LNCS, pages 324– 335. Springer, 2010.
  23. Daniel Angus, "Ant Colony Optimisation: From Biological Inspiration to an Algorithmic Framework," Technical Report No. TROl3, Swinburne University of Technology, Melbourne, Australia, 2006.
  24. M. Dorigo, V. Maniezzo, and A. Colorni, "Ant System: Optimization by a colony of cooperaing agents," IEEE Trans. on SMC, pp. 29-41. 1996.
  25. M. Dorigo, and C. Blumb, "Ant colony optimization theory: A survey," Theoretical Computer Science, vol. 344, pp. 243-278. 2005.
  26. Sun, Y, He, P. , Zhang, H. , et al. "Dynamic multicast QoS routing algorithm based on ant colony algorithm," Journal of Chongqing University of Posts and Telecommunications (Natural Science), 92-95, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

ACO multi congestion QOS hierarchical routing pheromone particle velocity

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