An Efficient Swarm-Based Multicast Routing Technique—Review

  • Priyanka KumariEmail author
  • Sudip Kumar Sahana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)


Multicast routing is emerging as a popular communication format for networks where a sender sends the same data packet to multiple nodes in the network simultaneously. To support this, it is important to construct a multicast tree having minimal cost for every communication session. But, because of dynamic and unpredictable environment of the network, multicast routing turns into a combinatorial issue to locate a best path connecting a source node and destination node having minimum distance, delay and congestion. To overcome this, various multicast conventions have been proposed. As of late, swarm and evolutionary techniques such as ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC) and genetic algorithm (GA) have been adopted by the researchers for multicast routing. Out of these, ACO and GA are most popular. This paper shows an important review of existing multicast routing techniques along with their advantages and limitations.


Multicast routing Ant colony optimization (ACO) Particle swarm optimization (PSO) Artificial bee colony (ABC) Genetic algorithm (GA) 


  1. 1.
    D. Sivakumar, B. Suseela, R.Varadharajan, “A Survey of Routing Algorithms for MANET”, IEEE- International Conference On Advances In Engineering, Science And Management (ICAESM- 2012), March 30, 31, 2012, pp. 625–640.Google Scholar
  2. 2.
    P. M. Pardalos and C. A. S. Oliveira, “A survey of combinatorial optimization problems in multicast routing,” Comput. Oper. Res., ELSEVIER, 2005, vol. 32, no. 8, p. 1953–1981 M. R. Macedonia, D. P. Brutzman, “MBone provides audio and video across the internet”, Computer, IEEE, 1994, Volume: 27, p. 30–36.Google Scholar
  3. 3.
    N. Al-Karaki and A.E. Kamal, “Routing techniques in wireless sensor networks: A survey”, IEEE Wireless Commun. Mag., Dec- 2004, vol.11, no.6, pp.-6–28.Google Scholar
  4. 4.
    Anuj K. Gupta, Harsh Saadawarti, and Anil K. Verma, “Review of various routing protocols for MANETs”, International Journal of information and Electronics, IEEE, vol. 1, No. 3, Nov-2011, p. 251–259.Google Scholar
  5. 5.
    T. Clausen, P. Jacquet, A. Laouiti, P. Muhlethaler, A. Qayyum, and L. Viennot, “Optimized link state routing protocol for ad-hoc networks” in: Proceedings of IEEE INMIC, December 2001, pp. 62–68.Google Scholar
  6. 6.
    Charles E. Perkins, Elizabeth M. Royer, “Ad-hoc On-Demand Distance Vector Routing”, In Proceedings of the 2nd IEEE Workshop on,” Mobile Computing Systems and Applications”, Feb-1999, pp. 90–100.Google Scholar
  7. 7.
    David A. Maltz, David B. Johnson, “Protocols for adaptive wireless and mobile computing.” In IEEE Personal Communications, 3(1), February 1996.Google Scholar
  8. 8.
    Z. J. Haas, M. Perlman, “The performance of query control schemes of the zone routing protocol”, IEEE/ACM Transactions on networking,” vol. 9. 4, pp. 427–438, Aug-2001.Google Scholar
  9. 9.
    Mehtab Alam, Asif Hameed Khan, Ihtiram Raza Khan, “Intelligence in MANETs: A Survey”, International Journal of Emerging Research in Management & Technology (IJERMT), May-2016, Vol-5, pp. 141–150.Google Scholar
  10. 10.
    Y. Yen, Y. Chan, H.Chao, J. H. park, “A genetic Algorithm for energy-efficient based Multicast Routing in MANETs”, ELSEVIER., pp. 858–869, Mar.-2008.Google Scholar
  11. 11.
    E. Bonabeau, M. Dorigo, G. Theraulaz, FROM Natural to Artificial Systems, Oxford university press, New York, NY, 1999.Google Scholar
  12. 12.
    C.E. Perkins and P. Bhagwat, “Highly Dynamic Destination-Sequenced Distance-Vector (DSDV) for Mobile Computer”, “Proc. ACM Conf. Communications Architectures and Protocols”, London, UK, August 1994, pp. 234–244.Google Scholar
  13. 13.
    S. Murthy and J. J. Garcia-Luna-Aceves, “An efficient routing protocol for wireless networks”, ACM Mobile Networks and App. J. Special Issue on Routing in Mobile Communication Networks, Oct. 1996, pp. 183–197.Google Scholar
  14. 14.
    Sanghoun Oh, Chang Wook Ahn and R.S. Ramakrishna, “A Genetic-Inspired Multicast Routing Optimization Algorithm with Bandwidth and End-to-End Delay Constraints”, SPRINGER, Berlin, Heidelberg, ICONIP, Part III, LNCS 4234, pp. 807–816.Google Scholar
  15. 15.
    Neeraj, A. Kumar, “Efficient Hierarchical Hybrids Parallel Genetic Algorithm For Shortest Path Routing”, Proceedings in 5th International Conference-Confluence the Next Generation Information Technology Summit (Confluence) IEEE, Sept.-2014, pp. 257–261.Google Scholar
  16. 16.
    E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence. FROM NATURAL to Artificial Systems, Oxford University press, New York, NY, 1999.Google Scholar
  17. 17.
    Marco Dorigo, Mauro Birattari, Thomas stutzle, Ant Colony Optimization”, IEEE Computational Intelligence Magazine, 2006, vol. 1, p. 28–39.CrossRefGoogle Scholar
  18. 18.
    Gianni Di Caro, Marco Dorigo, “Mobile Agents for Adaptive Routing”, Proceedings of the Thirty-First Hawaii International Conference on System Sciences, IEEE, 1998, PP. 74–83.Google Scholar
  19. 19.
    Mesut, Gunes, Udo Sorges, Imed Bouazizi, “ARA-The Ant-Colony Based Routing Algorithm for MANETs”, In Proceedings IEEE Computer Society ICPPW’02 Workshop, 2002.Google Scholar
  20. 20.
    Shivanjay Marwaha, Chen khong tham, Dipti Srinivasan, “A Novel Routing Protocol Using Mobile Agents and Reactive Route Discovery For AD-Hoc Wireless Networks”, Global Telecommunications Conference, 2002. GLOBECOM ‘02. IEEE, 2002 pp. 163–167.Google Scholar
  21. 21.
    O. Hossein and T. Saadawi, “Ant routing algorithm for mobile adhoc networks (ARAMA)”, Proceedings of the 22nd IEEE International Performance, Computing and Computing, and Communications Conference, Phoenix, Arizona, USA, April 2003, pp. 281–290.Google Scholar
  22. 22.
    J. Wang, E. Osagie, P. Thulasiraman, R. Thulasiram, “Hopnet: a hybrid ant colony optimization routing algorithm for mobile ad hoc network,” Ad Hoc Networks 7 (4), ELSEVIER, 2009, pp. 690–705.Google Scholar
  23. 23.
    S. K. Sahana, Mohammad AL-Fayoumi, P. K. Mahanti,: “Application of Modified Ant Colony Optimization (MACO) for Multicast Routing Problem”, I.J. Intelligent Systems and Applications, 2016, 4, p- 43–48.CrossRefGoogle Scholar
  24. 24.
    R. C. Eberhart, J. Kennedy, “A new optimizer using particle swarm theory”, Proceedings of the 6th Symposium Micro Machine and Human Science, IEEE Press, Los Alamitos, CA, October 1995, pp. 39–43.Google Scholar
  25. 25.
    Ziqiang Wang, Xia Sun, Dexian Zhang. “A PSO-Based Multicast Routing Algorithm”, 3rd International Conference On Natural Computation (ICNC), Computer Society, IEEE, 2007, p. 664–667.Google Scholar
  26. 26.
    Rehab F. Abdel Kader, “Hybrid discrete PSO with GA operators for efficient QoS-multicast routing”, Ain Shams Engineering Journal, Production and hosting by ELSEVIER, March 2011, pp. 21–31.Google Scholar
  27. 27.
    Manoj Kumar Patel *, Manas Ranjan Kabat, Chita Ranjan Tripathy, “A hybrid ACO/PSO based algorithm for QoS multicast routing problem”, Ain Shams Engineering Journal, ELSEVIER, 2014, pp. 113–120.Google Scholar
  28. 28.
    Karaboga, D., “An idea based on honey bee swarm for numerical optimization”, Tech. Rep. TR06, Erciyes Univ. Press, Erciyes, 2005.Google Scholar
  29. 29.
    Horst. F. Wedde and Muddassar, “The Wisdom Of The Hive Applied To Mobile Ad-Hoc Networks”, Proceedings IEEE Swarm Intelligence Symposium, 2005, pp. 341–348.Google Scholar
  30. 30.
    Zhenhua Zheng, Hua Wang, Lin Yao, “An artificial bee colony optimization algorithm for multicast routing”, 14th International Conference on advanced Communication Technology (ICACT), IEEE, 2012, pp. 168–172.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringBirla Institute of TechnologyMesra, RanchiIndia

Personalised recommendations