A Novel Bi-Velocity Particle Swarm Optimization Scheme for Multicast Routing Problem
PDF
PDF

How to Cite

Shakya, Subarna. 2020. “A Novel Bi-Velocity Particle Swarm Optimization Scheme for Multicast Routing Problem”. IRO Journal on Sustainable Wireless Systems 2 (1): 50-58. https://doi.org/10.36548/jsws.2020.1.006.

Keywords

— Steiner tree
— Particle swarm optimization
— multicast routing problem
— communication networks
— nondeterministic polynomial
Published: 25-04-2020

Abstract

A nondeterministic polynomial (NP) with complete Multicast routing problem is defined using a bi-velocity particle swarm optimization (BVDPSO) is proposed in this paper. The shift of particle swarm optimization to the discrete or binary domain, stepping away from the continuous domain is the major impact of the work. Initially a bi-velocity strategy is built such that it characterizes each dimension in terms of 0 and 1. The basic function of this strategy is to describe the MRP's binary characteristics such that 0 stands for the node not being selected while 1 stands for selection. Based on the location and velocity of the original PSO in the continuous domain, the BVDPSO is updated. This will preserve the global search ability and fast convergence speed of the original PSO. 58 instances of large, medium and small scales are used for experimentation in the OR-Library. Based on the results, it is identified that it is possible to get near-optimal or optimal solutions for BVDPSO as it requires generation of limited multicast trees. This approach is found to be optimal over its peers and outperforms recent heuristic algorithms and many advanced techniques used for the MRP problem. They also outperform several PSO, ant colony optimization and genetic algorithms.

References

  1. A. Sabbah, A. EI-Mougy, and M. Ibnkahla, “A survey of networking challenges and routing protocols in smart grids,” IEEE Trans. Ind. Informt., vol. 10, no. 1, pp. 210–221, Feb. 2014.
  2. G. Kandavanam, D. Botvich, S. Balasubramaniam, and C. Kulatunga, “PaCRAm: Path aware content replication approach with multicast for IPTV networks,” in Proc. IEEE Globecom, 2010, pp. 1–6.
  3. G. Kandavanam, D. Botvich, S. Balasubramaniam, and B. Jennings, “A hybrid genetic algorithm/variable neighborhood search approach to maximizing residual bandwidth of links for route planning,” in Proc. 9th Int. Conf. Artif. Evol., 2009, pp. 49–60.
  4. R. J. Wai, J. D. Lee, and K. L. Chuang, “Real-time PID control strategy for maglev transportation system via particle swarm optimization,” IEEE Trans. Ind. Electron., vol. 58, no. 2, pp. 629–646, Feb. 2011.
  5. Z. H. Zhan, J. Zhang, Y. Li, and Y. H. Shi, “Orthogonal learning particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 15, no. 6, pp. 832– 847, Dec. 2011.
  6. W. N. Chen, J. Zhang, Y. Lin, N. Chen, Z. H. Zhan, H. Chung, Y. Li, and Y. H. Shi, “Particle swarm optimization with an aging leader and challengers,” IEEE Trans. Evol. Comput., vol. Apr. 2013.
  7. K. Chan, T. Dillon, and E. Chang, “An intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems,” IEEE Trans. Ind. Electron., vol. 60, no. 10, pp. 4714–4725, Oct. 2013.
  8. H.Wang, X. X. Meng, S. Li, and H. Xu, “A tree-based particle swarm optimization for multicast routing,” Comput. Netw., vol. 54, no. 15, pp. 2775– 2786, Oct. 2010.
  9. R. Qu, Y. Xu, J. P. Castro, and D. Landa-Silva, “Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems,” J. Heuristics, vol. 19, no. 2, pp. 317–342, Apr. 2013.
  10. R. J. Wai, J. D. Lee, and K. L. Chuang, “Real-time PID control strategy for maglev transportation system via particle swarm optimization,” IEEE Trans. Ind. Electron., vol. 58, no. 2, pp. 629–646, Feb. 2011.
  11. H. P. Li and Y. Shi, “Network-based predictive control for constrained nonlinear systems with two-channel packet dropouts,” IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1574–1582, Mar. 2014.
  12. A. F. Zobaa, “Optimal multiobjective design of hybrid active power filters considering a distorted environment,” IEEE Trans. Ind. Electron., vol. 61, no. 1, pp. 107–114, Jan. 2014.