A Modified Particle Swarm Optimization Algorithm : Information Diffusion PSO

새로운 위상 기반의 Particle Swarm Optimization 알고리즘 : 정보파급 PSO

  • Park, Jun-Hyuk (Institute of Information Technology, Inc.) ;
  • Kim, Byung-In (Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH))
  • 박준혁 (미국 Institute of Information Technology사) ;
  • 김병인 (포항공과대학교 산업경영공학과)
  • Received : 2011.05.05
  • Accepted : 2011.06.07
  • Published : 2011.09.01


This paper proposes a modified version of Particle Swarm Optimization (PSO) called Information Diffusion PSO (ID-PSO). In PSO algorithms, premature convergence of particles could be prevented by defining proper population topology. In this paper, we propose a variant of PSO algorithm using a new population topology. We draw inspiration from the theory of information diffusion which models the transmission of information or a rumor as one-to-one interactions between people. In ID-PSO, a particle interacts with only one particle at each iteration and they share their personal best solutions and recognized best solutions. Each particle recognizes the best solution that it has experienced or has learned from another particle as the recognized best. Computational experiments on the benchmark functions show the effectiveness of the proposed algorithm compared with the existing methods which use different population topologies.


  1. Anghinolfi, D. and Paolucci, M. (2009), A new discrete particle swarm optimization approach for the single-machine total weighted tardiness scheduling problem with sequence-dependent setup times, European Journal of Operational Research, 193(1), 73-85.
  2. Clerc, M. and Kennedy, J. (2002), The particle swarm explosion, stability, and convergence in a multidimensional complex space, IEEE Transaction on Evolutionary Computation, 6, 58-73.
  3. Kawachi, K., Seki, M., Yoshida, H., Otake, Y., Warashina, K., and Ueda, H. (2008), A rumor transmission model with various contact interactions, Journal of Theoretical Biology, 253(1), 55-60.
  4. Kennedy, J. and Eberhart, R. C. (1995), Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, IV, 1942-1948.
  5. Kennedy, J. (1999), Small worlds and mega-minds : effects of neighborhood topology on particle swarm performance, Proceedings of IEEE Congress on Evolutionary Computation.
  6. Kennedy, J. and Mendes, R. (2002), Topological structure and particle swarm performance, Proceedings of the Fourth Congress on Evolutionary Computation (CEC-2002), Honolulu, Hawaii, 12-17.
  7. Liang, J. J. and Suganthan, P. N. (2005), Dynamic multi-swarm particle swarm optimizer, Proceedings of the IEEE International Swarm Intelligence Symposium.
  8. Liu, D. S., Tan, K. C., Huang, S. Y., and Goh, C. K. (2008), On solving multiobjective bin packing problems using evolutionary particle swarm optimization, European Journal of Operational Research, 190(2), 357-382.
  9. Locatelli, M. (2003), A note on the Griewank test function, Journal of Global Optimization, 25, 169-174.
  10. Lovbjerg, M., Rasmussen, T. K., and Krink, T. (2001), Hybrid particle swarm optimser with breeding and subpopulations, Proceedings of the Genetic and Evolutionary Computation Conference.
  11. Mendes, R. (2004), Population topologies and their influence in particle swarm performance, PhD dissertation, University of Minho, Braga, Portugal.
  12. Mendes, R., Kennedy, J., and Neves, J. (2004), The fully informed particle swarm : simpler, maybe better, IEEE Transactions on Evolutionary Computation, 8(3), 204-210.
  13. Thompson, M. (1979), Information diffusion in populations with immigration, Information Sciences, 17, 113-130.
  14. Tseng, C.-T. and Liao C.-J. (2008), A discrete particle swarm optimization for lot-streaming flowshop scheduling problem, European Journal of Operational Research, 191(2), 360-373.
  15. Unler, A. and Murat, A. (2010), A Discrete Particle Swarm Optimization Method for Feature Selection in Binary Classification Problems, European Journal of Operational Research, 206(3), 528-539.