Skip to main content
SpringerLink
Log in

Particle Swarm Optimization with Watts-Strogatz Model

  • Conference paper
Swarm, Evolutionary, and Memetic Computing (SEMCCO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6466))

Included in the following conference series:

  • 2518 Accesses

Abstract

Particle swarm optimization (PSO) is a popular swarm intelligent methodology by simulating the animal social behaviors. Recent study shows that this type of social behaviors is a complex system, however, for most variants of PSO, all individuals lie in a fixed topology, and conflict this natural phenomenon. Therefore, in this paper, a new variant of PSO combined with Watts-Strogatz small-world topology model, called WSPSO, is proposed. In WSPSO, the topology is changed according to Watts-Strogatz rules within the whole evolutionary process. Simulation results show the proposed algorithm is effective and efficient.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of 6th International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)

    Google Scholar 

  2. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE CS Press, Perth (1995)

    Google Scholar 

  3. Senthil Arumugam, M., Ramana Murthy, G., Loo, C.K.: On the optimal control of the steel annealing processes as a two-stage hybrid systems via PSO algorithms. International Journal of Bio-inspired Computation 1(3), 198–209 (2009)

    Article  Google Scholar 

  4. Sivanandam, S.N., Visalakshi, P.: Dyanmic task scheduling with load balancing using parallel orthogonal particle swarm optimization. International Journal of Bio-inspired Computation 1(4), 276–286 (2009)

    Article  Google Scholar 

  5. Chen, S., Hong, X., Luk, B.L., Harris, C.: Non-linear system identification using particle swarm optimization tuned radial basis function models. International Journal of Bio-inspired Computation 1(4), 246–258 (2009)

    Article  Google Scholar 

  6. Suganthan, P.N.: Particle swarm optimiser with neighbourhood operator. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1958–1962. IEEE Service Center, Los Alamitos (1999)

    Google Scholar 

  7. Peer, E.S., van den Bergh, F., Enggelbrecht, A.P.: Using neighbourhoods with the guaranteed convergence PSO. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 235–242. IEEE Service Center, Los Alamitos (2003)

    Google Scholar 

  8. Mu, H.P.: Study on particle swarm optimization based on dynamic neighborhood topology, Master Dissertation, Taiyuan University of Science and Technology (2008)

    Google Scholar 

  9. Cui, Z.H., Chu, Y.F., Cai, X.J.: Nearest neighbor interaction PSO based on small-world model. In: Proceedings of 10th International Conference on Intelligent Data Engineering and Automated Learning, pp. 633–640. Springer, Heidelberg (2009)

    Google Scholar 

  10. Hamdan, S.A.: Hybrid Particle Swarm Optimiser using multi-neighborhood topologies. INFOCOMP Journal of Computer Science 7(1), 36–44 (2008)

    Google Scholar 

  11. Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 6684(393), 409–410 (1998)

    MATH  Google Scholar 

  12. Watts, D.J.: Small Worlds: The Dynamics of Networks Between Order and Randomness. Princeton University Press, Princeton (1999)

    MATH  Google Scholar 

  13. Yao, X., Liu, Y., Lin, G.M.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 82–102 (1999)

    Google Scholar 

  14. Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm opitmizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation 8(3), 240–255 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Economic Information Department, Shanxi Finance & Taxation College, No.25 QianFeng South Road, Wan Bailin District, Taiyuan, 030024, P.R. China

    Zhuanghua Zhu

Authors
  1. Zhuanghua Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhu, Z. (2010). Particle Swarm Optimization with Watts-Strogatz Model. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17563-3_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17562-6

  • Online ISBN: 978-3-642-17563-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation