Self-Organization Particle Swarm Optimization Based on Information Feedback

  • Jing Jie
  • Jianchao Zeng
  • Chongzhao Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


The paper develops a self-organization particle swarm optimization (SOPSO) with the aim to alleviate the premature convergence. SOPSO emphasizes the information interactions between the particle-lever and the swarm-lever, and introduce feedback to simulate the function. Through the feedback information, the particles can perceive the swarm-lever state and adopt favorable behavior model to modify their behavior, which not only can modify the exploitation and the exploration of the algorithm adaptively, but also can vary the diversity of the swarm and contribute to a global optimum output in the swarm. Relative experiments have been done; the results show SOPSO performs very well on benchmark problems, and outperforms the basic PSO in search ability.


Particle Swarm Optimization Particle Swarm Premature Convergence Unimodal Function Multimodal Function 
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.


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  1. 1.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Conference on Neural Networks, vol. 11, pp. 1942–1948. IEEE Service Center, Perth, Australia (1995)Google Scholar
  2. 2.
    Bergh, F.V.D., Engelbrecht, A.: Particle swarm weight initialization in multi-layer perception artificial neural networks. In: Development and Practice of Artificial Intelligence Techniques, Durban, South Africa, pp. 41–45 (1999)Google Scholar
  3. 3.
    Bergh, F.V.D., Engelbrecht, A.P.: Cooperative Learning in Neural Networks using Particle Swarm Optimizers. South African Computer Journal 26(11), 84–90 (2000)Google Scholar
  4. 4.
    Clerc, M., Kennedy, J.: The Particle Swarm–Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  5. 5.
    Fukuyama, Y., Yoshida, H.: A Particle Swarm Optimization for Reactive Power and Voltage Control in Electric Power Systems. In: Proc. Congress on Evolutionary Computation, Seoul, Korea, pp. 87–93. IEEE Service Center, Piscataway (2001)Google Scholar
  6. 6.
    Zeng, J.C., Jie, J., Cui, Z.H.: Particle Swarm Optimization. Science Press, Beijing (2004)Google Scholar
  7. 7.
    Suganthan, P.N.: Particle Swarm Optimizer with Neighborhood Operator. In: Proc. Congress on Evolutionary Computation, Washington D.C, USA, July, pp. 1958–1961. IEEE Service Center, Piscataway (1999)Google Scholar
  8. 8.
    Li, X.D.: Adaptively Choosing Neighborhood Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 105–116 (2004)Google Scholar
  9. 9.
    Xie, X.F., Z, W.J., B, D.C.: Optimizing Semiconductor Devices by Self-organizing Particle Swarm, Congress on Evolutionary Computaion, Oregon,USA, pp. 2017–2022 (2004)Google Scholar
  10. 10.
    Jacques, R., Jakob, S.V.: A Diversity-Guided Particle Swarm Optimizer –the ARPSO,
  11. 11.
    Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc. Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)Google Scholar
  12. 12.
    Kennedy, J.: Small Worlds and Mega-Minds: Effects of Neighborhood Topology on Particle Swarm Performance. In: Proceedings of the Congress of Evolutionary Computation, vol. 3, pp. 1931–1938. IEEE Press, Los Alamitos (1938)Google Scholar
  13. 13.
    Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proc. Congress on Evolutionary Computation, pp. 1945–1950. IEEE Service Center, Piscataway (1999)Google Scholar
  14. 14.
    Shi, Y., Eberhart, R.C.: Fuzzy Adaptive Particle Swarm Optimization. In: Proc. Congress on Evolutionary Computation, pp. 101–106. IEEE service Center, Seoul, Korea (2001)Google Scholar
  15. 15.
    Ursem, R.K.: Diversity-Guided Evolutionary Algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 462–474. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Iwasaki, N., Yasuda, K.: Adaptive Particle Swarm Optimization via Velocity Feedback. In: The 36th ISCIE International symposium on Stochastic Systems Theory and Its Applications, B7-5, pp. 116–117 (2004)Google Scholar
  17. 17.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence From Natural to Artificial Systems, pp. 1–22. Oxford University Press Inc., Oxford (1999)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jing Jie
    • 1
    • 2
  • Jianchao Zeng
    • 2
  • Chongzhao Han
    • 1
  1. 1.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an CityChina
  2. 2.Division of System Simulation & Computer ApplicationTaiyuan University of Science & TechnologyTaiyuan CityChina

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