A Particle Swarm Optimization Algorithm for the Multicast Routing Problem

  • Yannis Marinakis
  • Athanasios MigdalasEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 104)


In this paper, a new algorithm for the solution of the Multicast Routing Problem based on Particle Swarm Optimization algorithm is presented and analyzed. A review of the most important evolutionary optimization algorithms for the solution of this problem is also given. Three different versions of the proposed algorithm are given and their quality is evaluated with experiments conducted on suitably modified benchmark instances of the Euclidean Traveling Salesman Problem from the TSP library. The results indicated the efficiency of the proposed method.


Particle Swarm Optimization Local Search Destination Node Particle Swarm Optimization Algorithm Steiner Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Aarts, E.H.L., Verhoeven, M.G.A.: Genetic local search for the traveling salesman problem. In [3], chapter G9.5, G9.5:1–G9.5:7Google Scholar
  2. 2.
    Arabshahi, P., Gray, A., Kassabalidis, I., Das, A.: Adaptive routing in wireless communication networks using swarm intelligence. In: Proceedings of the 19th AIAA Int. Commun. Satellite Syst. Conf. (2001)Google Scholar
  3. 3.
    Bäck, T., Fogel, D.B., Michalewicz, Z. (Eds.): Handbook of Evolutionary Computation. Oxford University Press, Oxford (1997)zbMATHGoogle Scholar
  4. 4.
    Banks, A., Vincent, J., Anyakoha, C.: A review of particle swarm optimization. Part I: background and development. Nat. Comput. 6(4), 467–484 (2007)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Banks, A., Vincent, J., Anyakoha, C.: A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat. Comput. 7, 109–124 (2008)Google Scholar
  6. 6.
    Baras, J.S., Mehta, H.: A probabilistic emergent routing algorithm for mobile ad hoc networks. In: WiOpt’03: Modeling and Optimization in Mobile, AdHoc and Wireless Networks, Sophia-Antipoli, France, INRIA, March 3–5 2003Google Scholar
  7. 7.
    Cauvery, N.K., Viswanatha, K.V.: Routing in dynamic network using ants and genetic algorithm. Int. J. Comput. Sci. Network Secur. 9(3), 194–200 (2009)Google Scholar
  8. 8.
    Chow, C.H.: On multicast path finding algorithms. In: IEEE INFOCOM’91, pp. 1974–1283. IEEE, San Francisco (1991)Google Scholar
  9. 9.
    Clerc, M.: Particle Swarm Optimization. ISTE, London (2006)CrossRefzbMATHGoogle Scholar
  10. 10.
    Clerc, M., Kennedy, J.: The particle swarm: explosion, stability and convergence in a multi-dimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)CrossRefGoogle Scholar
  11. 11.
    Crichigno, J., Baran, B.: Multiobjective multicast routing algorithm for traffic engineering. In: Proceedings of the 13th International Conference on Computer Communication Networks, CCCN 2004, pp. 301–306. IEEE, San Francisco (2004)Google Scholar
  12. 12.
    Crichigno, J., Baran, B.: Multiobjective multicast routing algorithm. In: Lorenz, P., de Souza, J.N., Dini, P. (eds.), Telecommunications and Networking - ICT 2004. 11th International Conference on Telecommunications, Fortaleza, Brazil, August 1–6, 2004. Proceedings, vol. 3124 of Lecture Notes in Computer Science, pp. 1029–1034. Springer, New York (2004)Google Scholar
  13. 13.
    Curran, E.: Swarm: Cooperative reinforcement learning for routing in ad-hoc networks. Master’s thesis, University of Dublin, Trinity College, September (2003)Google Scholar
  14. 14.
    Di Caro, G.: Ant colony optimization and its application to adaptive routing in telecommunication networks. PhD thesis, Université Libre de Bruxelles, Faculté des Sciences Appliquées, September (2004)Google Scholar
  15. 15.
    Di Caro, G., Dorigo, M.: Antnet: Distributed stigmergetic control for communication networks. J. Artif. Intell. Res. 9, 317–365 (1998)zbMATHGoogle Scholar
  16. 16.
    Di Caro, G., Dorigo, M.: Two ant colony algorithms for best-effort routing in datagram network. In: Proceedings of the 10th IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS), (1998)Google Scholar
  17. 17.
    Di Caro, G., Dorigo, M.: Ant colonies for adaptive routing in packet-switched communications networks. In Eiben, A.E., Bäck, Th., Schoenauer, M., Schwefel, H.-P. (eds.), Parallel Problem Solving from Nature. PPSN V, vol. 1498 of Lecture Notes in Computer Science, pp. 673–682. Springer, New York (1998)Google Scholar
  18. 18.
    Di Caro, G.A., Ducatelle, F., Gambardella, L.M.: Theory and practice of ant colony optimization for routing in dynamic telecommunications networks. In: Sala, N., Orsucci, F. (eds.), Reflecting Interfaces: The Complex Coevolution of Information Technology Ecosystems, pp. 185–216. Idea Group, Hershey (2008)CrossRefGoogle Scholar
  19. 19.
    Doar, M., Leslie, I.: How bad is naive multicast routing. In: INFOCOM’93. Proceedings. Twelfth Annual Joint Conference of the IEEE Computer and Communications Societies. Networking: Foundation for the Future, pp. 82–89. IEEE, San Francisco (1993)Google Scholar
  20. 20.
    Ducatelle, F., Di Caro, G., Gambarella, L.M.: Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intell. 4(3), 173–198 (2010)CrossRefGoogle Scholar
  21. 21.
    Engelbrecht, A.P.: Computational Intelligence: An Introduction, 2nd edn. Wiley, Chichester (2007)CrossRefGoogle Scholar
  22. 22.
    Fabregat, R., Donoso, Y., Solano, F., Marzo, J.L.: Multitree routing for multicast flows: A genetic algorithm approach. In: Vitriá, J., Radeva, P., Aguiló, I. (eds.), Recent Advances in Artificial Intelligence Research and Development, pp. 399–405. IOS Press, Amsterdam (2004)Google Scholar
  23. 23.
    Farooq, M., Di Caro, G.: Routing protocols for next generation networks inspired by collective behaviors of insect societies: An overview. In: Blum, C., Merkle, D. (eds.), Swarm Intelligence: Introduction and Applications, pp. 101–160. Springer, New York (2008)CrossRefGoogle Scholar
  24. 24.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)zbMATHGoogle Scholar
  25. 25.
    Hansen, P., Mladenović, N.: Variable neighborhood search: Principles and applications. Eur. J. Oper. Res. 130, 449–467 (2001)CrossRefzbMATHGoogle Scholar
  26. 26.
    Hansen, P., Mladenović, N., Moreno-Pérez, J.A.: Variable neighbourhood search: methods and applications. Ann. Oper. Res. 175, 367–407 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Hwang, R.-H., Do, W.-Y., Yang, S.-C.: Multicast routing based on genetic algorithms. In: WiOpt’03: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks. INRIA Sophia-Antipolis, France, March 3–5, 2003Google Scholar
  28. 28.
    Ibaraki, T.: Combination with local search. In: [3], chapter G3.2, D3.2:1–D3.2:5Google Scholar
  29. 29.
    Ibaraki, T.: Simulated annealing and tabu search. In: [3], chapter D3.5, D3.5:1–D3.5:2Google Scholar
  30. 30.
    Kassabalidis, I., El-Sharkawi, M.A., Marks II, R.J., Arabshahi, P., Gray, A.A.: Swarm intelligence for routing in communication networks. In: Proceedings of the IEEE Globecom 2001, San Antonio, Texas (2001).Google Scholar
  31. 31.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  32. 32.
    Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm, In: Proceedings of 1997 IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104–4108 (1997)Google Scholar
  33. 33.
    Kompleea, V.P., Pasquale, J.C., Polyzos, G.C.: Multicast routing for multimedia communication. IEEE/ACM Trans. Network. 1(3), 286–292 (1993)CrossRefGoogle Scholar
  34. 34.
    Mukherjee, D., Acharyya, S.: Ant colony optimization techniques applied in network routing problem. Int. J. Comput. Appl. 1(15), 66–73 (2010)Google Scholar
  35. 35.
    Munetomo, M.: The genetic adaptive routing algorithm. In: Corne, D.W., Oates, M.J., Smith, G.D. (eds.), Telecommunications Optimization: Heuristic and Adaptive Techniques, pp. 151–166. Wiley, Chichester (2000)Google Scholar
  36. 36.
    Oh, J., Pyo, I., Pedram, M.: Constructing minimal spanning/steiner trees with bounded path length. In: European Design and Test Conference, pp. 244–249 (1996)Google Scholar
  37. 37.
    Oliveira, C.A.S., Pardalos, P.M.: A survey of combinatorial optimization problems in multicast routing. Comput. Oper. Res. 32(8), 1953–1981 (2005)CrossRefzbMATHGoogle Scholar
  38. 38.
    Oliveira, C.A.S., Pardalos, P.M., Resende, M.G.C.: Optimization problems in multicast tree construction. In: [43], 701–731Google Scholar
  39. 39.
    Pinto, D., Barán, B.: Multiobjective multicast routing with ant colony optimization. In: Gaiti, D. (ed.), Network Control and Engineering for QoS, Security and Mobility V, vol. 213 of IFIP International Federation of Information Processing, pp. 101–115. Springer, New York (2006)Google Scholar
  40. 40.
    Pinto, D., Barán, B., Fabregat, R.: Multi-objective multicast routing based on ant colony optimization. In: López, B., Meléndez, J., Radeva, P., Vitriá, J. (eds), Artificial Intelligence Research and Development, pp. 363–370. IOS Press, Amsterdam (2005)Google Scholar
  41. 41.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. an overview. Swarm Intell. 1, 33–57 (2007)Google Scholar
  42. 42.
    Purkayastha, P.: Multipath routing algorithms for communication networks: ant routing and optimization based approaches. PhD thesis, Department of Electrical and Computer Engineering, University of Meryland (2009)Google Scholar
  43. 43.
    Resende, M.G.C., Pardalos, P.M. (eds.): Handbook of Optimization in Telecommunications. Springer, New York (2006)zbMATHGoogle Scholar
  44. 44.
    Saleem, M., Di Caro, G.A., Farooq, M.: Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions. Inform. Sci. 181, 4597–4624 (2011)CrossRefGoogle Scholar
  45. 45.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of 1998 IEEE World Congress on Computational Intelligence, pp. 69–73 (1998)Google Scholar
  46. 46.
    Sigel, E., Denby, B., Le Hégarat-Mascle, S.: Application of ant colony optimization to adaptive routing in a leo telecommunications satellite network. Annales des Télecommunications 57(5–6), 520–539 (2002)Google Scholar
  47. 47.
    Takahashi, H., Mutsuyama, A.: An approximate solution for the steiner problem in graphs. Mathematica Japonica 6, 573–577 (1980)Google Scholar
  48. 48.
    Tode, H., Sakai, Y., Yamamoto, M., Okada, H., Tezuka, Y.: Multicast routing algorithm for nodal load balancing. In: IEEE INFOCOM’92, pp. 2086–2095. IEEE, San Francisco (1992)Google Scholar
  49. 49.
    Waxman, B.M.: Routing of multipoint connections. IEEE J. Sel. Area Comm. 1(3), 286–292 (1988)Google Scholar
  50. 50.
    Wu, J.J., Hwang, R.-H.: Multicast routing with multiple constraints. Inform. Sci. 124, 29–57 (2000)CrossRefGoogle Scholar
  51. 51.
    Xu, Y., Qu, R., Li, R.: A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems. Ann. Oper. Res. 1–29 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Technical University of Crete, School of Production Engineering and ManagementDecision Support Systems LaboratoryChaniaGreece
  2. 2.Department of Civil EngineeringAristotle University of ThessalonikeThessalonikeGreece
  3. 3.Industrial LogisticsLuleå Technical UniversityLuleåSweden

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