Skip to main content

Advertisement

Springer Nature Link
Log in

Self-organization of Decentralized Swarm Agents Based on Modified Particle Swarm Algorithm

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

In this paper, an attempt has been made by incorporating some special features in the conventional particle swarm optimization (PSO) technique for decentralized swarm agents. The modified particle swarm algorithm (MPSA) for the self-organization of decentralized swarm agents is proposed and studied. In the MPSA, the update rule of the best agent in swarm is based on a proportional control concept and the objective value of each agent is evaluated on-line. In this scheme, each agent self-organizes to flock to the best agent in swarm and migrate to a moving target while avoiding collision between the agent and the nearest obstacle/agent. To analyze the dynamics of the MPSA, stability analysis is carried out on the basis of the eigenvalue analysis for the time-varying discrete system. Moreover, a guideline about how to tune the MPSA's parameters is proposed. The simulation results have shown that the proposed scheme effectively constructs a self-organized swarm system in the capability of flocking and migration.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Anderson, G.T., Clark, M.R.: Navigation of autonomous robots with an intelligent Oscillator Controller. In: Proc. of the IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, pp. 1003–1007 (1999)

  2. Balch, T., Hybinette, M.: Behavior-based coordination of large-scale robot formations. In: Proc. Fourth Int. Conf. on Multi Agent Systems, pp. 363–364 (2000)

  3. Beni, G.: The concept of cellular robotic system. In: IEEE Int. Symp. on Intelligent Control, pp. 57–62 (1998)

  4. Carlisle, A., Dozier, G.: Adapting particle swarm optimization to dynamic environments. In: Proceedings 2000 ICAI, Las Vegas, Nevada, vol. I, pp. 429–434 (2000)

  5. Clark, M.R., Anderson, G.T.: Coupled Oscillator control of autonomous mobile robots. Auton. Robots 9, 189–198 (2000)

    Article  Google Scholar 

  6. Eberhart, R., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: The 7th Annual Conf. on Evolutionary Programming, pp. 611–616 (1998)

  7. Fukuyama, Y., et al.: A particle swarm optimization for reactive power and voltage control considering voltage stability. In: Proc. of IEEE Int. Conf. on Intelligent System Application to Power Systems (ISAP), Rio de Janeiro, April 1999

  8. Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge, UK, (1985)

    MATH  Google Scholar 

  9. Jin, K., Liang, P., Beni, G.: Stability of synchronized distributed control of discrete swarm structures. In: IEEE Int. Conf. on Robotics and Automation, pp. 1033–1038 (1994)

  10. Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proc. of IEEE Int. Conf. on Evolutionary Computation, pp. 303–308 (1997)

  11. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942–1948 (1995)

  12. Kennedy, J., Spears, W.: Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In: IEEE Int. Conf. on Evolutionary Computation, pp. 78–83 (1998)

  13. Kim, D.H.: A swarm system design based on coupled nonlinear oscillators for cooperative behavior. Int. J. Control. Autom. Syst. 1(3), 301–307 (2003)

    Google Scholar 

  14. Levine, H., Rappel, W., Cohen, I.: Self-organized in systems of self-propelled particles. Phys. Rev. E 63, 017101(4) (2001)

    Article  Google Scholar 

  15. Liang, P. and Beni, G.: Robotic morphogenesis. In: IEEE Int. Conf. on Robotics and Automation, pp. 2175–2180 (1995)

  16. Mogilner, A., Edelstin-Keshet, L.: Spatio-angular order in population of self-aligning objects: Formation of oriented patches. Physica D 89, 346–367 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  17. Nolfi, S., Floreano, D.: Evolutionary Robotics. MIT, Cambridge, MA (2000)

    Google Scholar 

  18. Parker, L.E.: Cooperative robotics for multi-target observation. Intelligent Automation and Soft Computing, special issue on Robotics Research at Oak Ridge National Laboratory 5(1), 5–19 (1999)

  19. Reynolds, C.W.: An evolved vision-based behavioral model of coordinated group motion. In: Meyer, J.-A., Roiblat, H.L., Wilson, S. (eds.) From Animals to Animals 2, pp 384–392. MIT, Cambridge, MA (1982)

    Google Scholar 

  20. Reynolds, C.W.: Flocks, herds and schools: A distributed behavioral model. Comput. Graph. 21(4), 25–34 (1987)

    Article  MathSciNet  Google Scholar 

  21. Selvan, S.E., Subramanian, S., Solomon, S.T.: Novel technique for PID tuning by particle swarm optimization. In: Seventh Annual Swarm Users/Researchers Conference, http://www.nd.edu/~swarm03/ (2003)

  22. Shi, Y.H., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc. of IEEE Int. Conf. on Evolutionary Computation, pp. 69–73 (1998)

  23. Werger, B.B., Mataric, M.J.: Broadcast of local eligibility for multi-target observation. In: Proc. on Distributed Autonomous Robotic Systems, pp. 347–356 (2000)

  24. Yasuda, K., Ide A. and Iwasaki, N.: Adaptive particle swarm optimization using velocity information of swarm. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 3475–3481 (2004)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Kyungnam University, Masan, 631-701, South Korea

    Dong H. Kim

  2. School of Information Science and Technology, The University of Tokyo, Tokyo, 113-8656, Japan

    Seiichi Shin

Authors
  1. Dong H. Kim
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Seiichi Shin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Dong H. Kim.

Additional information

Category (5) – Intelligent Systems / Intelligent Control / Fuzzy Control / Prosthetics / Robot Motion Planning

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, D.H., Shin, S. Self-organization of Decentralized Swarm Agents Based on Modified Particle Swarm Algorithm. J Intell Robot Syst 46, 129–149 (2006). https://doi.org/10.1007/s10846-006-9047-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-006-9047-3

Key words