Skip to main content
SpringerLink
Log in

ACONN—A Multicast Routing Implementation

  • Conference paper
  • First Online:
Computational Intelligence in Data Mining—Volume 2

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 411))

Abstract

In a communication network the biggest challenge with multicasting is minimizing the amount of network resources employed. This paper proposes an ant colony optimization (ACO) and neural network (NN) based novel ACONN implementation for an efficient use of multicast routing in a communication network. ACO globally optimize the search space where as NN dynamically determine the effective path for multicast problem. The number of iteration and complexity study shows that the proposed hybrid technique is more cost effective and converges faster to give optimal solution for multicast routing in comparison to ACO and Dijkstra’s algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chen, X., Liu, S., Guan, J., Liu, Q.: Study on QoS multicast routing based on ACO-PSO algorithm. In: International Conference on Intelligent Computation Technology and Automation (ICICTA), vol. 3, pp. 534–537 (2010)

    Google Scholar 

  2. Li, C., Cao, C., Li, Y., Yu, Y.: Hybrid of genetic algorithm and particle swarm optimization for multicast QoS routing. In: IEEE International Conference Controls Automation, pp. 2355–2359 (2007)

    Google Scholar 

  3. Wang, H., Meng, X., Zhang, M., Li, Y.: Tabu search algorithm for RP selection in PIM-SM multicast routing. Elsevier Comput. Commun. 33, 35–42 (2009)

    Article  Google Scholar 

  4. Wang, H., Meng, X., Li, S., Xu, H.: A tree-based particle swarm optimization for multicast routing. Comput. Netw. 54(15), 2775–2786 (2010)

    Article  MATH  Google Scholar 

  5. Zhou, J., Cao, Q., Li, C., Huang, R.: A genetic algorithm based on extended sequence and topology encoding for the multicast protocol in two-tiered WSN. Expert Syst. Appl. 37(2), 1684–1695 (2010)

    Article  Google Scholar 

  6. Wang, H., Xu, H., Yi, S., Shi, Z.: A tree-growth based ant colony algorithm for QoS multicast routing problem. Expert Syst. Appl. 38, 11787–11795 (2011)

    Article  Google Scholar 

  7. Patel. M.K., Kabat, M.R., Tripathy, C.R.: A hybrid ACO/PSO based algorithm for QoS multicast routing problem. Ain Shams Eng. J. 5(1), 113–120 (2014)

    Google Scholar 

  8. Shimamoto, N., Hiramatsu, A., Yamasaki, K.: A dynamic routing control based on a GA. In: Proceedings of the IEEE International Conference on Neural Network, pp. 1123–1128 (1993)

    Google Scholar 

  9. Zhang, L., Cai, L., Li, M., Wang, F.: A method for least-cost QoS least-cost multicast routing based on genetic simulated annealing algorithm. Comput. Commun. 31, 3984–3994 (2008)

    Google Scholar 

  10. Frank, A.J., Wittie, L.D., Bernstein, A.J.: Multicast communication on network computers. IEEE Softw. 2(3), 49–61 (1985)

    Article  Google Scholar 

  11. Yen, J.Y.: An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Q. Appl. Math. 27, 526–530 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  12. Wang, Y., Xie, J.: Ant colony optimization for multicast routing. IEEE APCCAS (2000)

    Google Scholar 

  13. Yuan, P., Hai, Y.: An improved ACO algorithm for multicast in ad hoc networks. In: International Conference on Communications and Mobile Computing (2010)

    Google Scholar 

  14. Wang, H., Shi, Z., Li, S.: Multicast routing for delay variation bound using a modified ant Colony algorithm. J. Netw. Comput. Appl. (2008)

    Google Scholar 

  15. Pan, D.-R., Xue, Y., Zhan, L.-J.: A multicast wireless mesh network (WMN) network routing algorithm with ant colony optimization. In: International Conference on Wavelet Analysis and Pattern Recognition, pp. 744–748 (2008)

    Google Scholar 

  16. Singh, G., Kumar, N., Verma, A.K.: Ant colony algorithms in MANETs: a review. J. Netw. Comput. Appl. 35, 1964–1972 (2012)

    Article  Google Scholar 

  17. Wang, H., Xu, H., Yi, S., Shi, Z.: A tree-growth based ant colony algorithm for QoS multicast routing problem. Exp. Syst. Appl. 38, 11787–11795 (2011)

    Article  Google Scholar 

  18. Huang, Y.: Research on QoS multicast tree based on ant colony algorithm. Appl. Mech. Mater. 635–637; 1734–1737 (2014)

    Google Scholar 

  19. Liu, W., Wang, L.: Solving the delay constrained multicast routing problem using the transiently chaotic neural network. Advances in Neural Networks. Lecture Notes in Computer Science, vol. 4492, pp. 57–62

    Google Scholar 

  20. Pour, H.M., Atlasbaf, Z., Mirzaee, A., Hakkak, M.: A hybrid approach involving artificial neural network and ant colony optimization for direction of arrival estimation. In: Canadian Conference on Electrical and Computer Engineering, pp. 001059–001064 (2008)

    Google Scholar 

  21. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26, 29–41 (1996)

    Article  Google Scholar 

  22. Srivastava, S., Sahana, S.K., Pant, D., Mahanti, P.K.: Hybrid microscopic discrete evolutionary model for traffic signal optimization. J. Next Gener. Inf. Technol. (JNIT) 6(2), 1–6 (2015)

    Google Scholar 

  23. Sahana, S.K., Jain, A., Mahanti, P.K.: Ant colony optimization for train scheduling: an analysis. I. J. Intell. Syst. Appl. 6(2), 29–36 (2014)

    Google Scholar 

  24. Sahana, S.K., Jain, A.: High performance ant colony optimizer (HPACO) for travelling salesman problem (TSP). In: 5th International Conference on ICSI 2014, Hefei, China, October 17–20, 2014, In: Advances in Swarm Intelligence, vol. 8794, Springer International Publishing, Lecture Notes in Computer Science (LNCS), pp. 165–172 (2014)

    Google Scholar 

  25. Sahana, S.K., Jain, A.: An improved modular hybrid ant colony approach for solving traveling salesman problem. Int. J. Comput. (JoC) 1(2), 123–127 (2011)

    Google Scholar 

  26. Mehmet Ali, M.K., Kamoun, F.: Neural networks for shortest path computation and routing in computer networks. IEEE Trans. Neural Netw. 4(6), 941–954 (1993)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, Birla Institute of Technology, Mesra, India

    Sweta Srivastava & Sudip Kumar Sahana

Authors
  1. Sweta Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sudip Kumar Sahana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sweta Srivastava .

Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India

    Durga Prasad Mohapatra

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this paper

Cite this paper

Srivastava, S., Sahana, S.K. (2016). ACONN—A Multicast Routing Implementation. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 2. Advances in Intelligent Systems and Computing, vol 411. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2731-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2731-1_12

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2729-8

  • Online ISBN: 978-81-322-2731-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation