Skip to main content
SpringerLink
Log in
SpringerLink
Log in

New Adaptive Approach for Chaos PSO Algorithm Driven Alternately by Two Different Chaotic Maps – An Initial Study

  • Conference paper
Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 210))

Abstract

In this initial study a novel adaptive approach for chaos driven PSO algorithm is proposed. Two different chaotic maps are used as pseudorandom number generators and switched over during the run of chaos driven PSO algorithm. The new adaptive approach brings promising results that are presented and briefly analyzed.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV (1948)

    Google Scholar 

  2. Dorigo, M.: Ant Colony Optimization and Swarm Intelligence. Springer (2006)

    Google Scholar 

  3. Eberhart, R., Kennedy, J.: Swarm Intelligence. The Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  4. Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning, p. 41. Addison Wesley (1989) ISBN 0201157675

    Google Scholar 

  5. Storn, R., Price, R.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  6. Zelinka: SOMA - self organizing migrating algorithm. In: Babu, B.V., Onwubolu, G. (eds.) New Optimization Techniques in Engineering, vol. 33, ch.7. Springer (2004) ISBN: 3-540-20167X

    Google Scholar 

  7. Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.G.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7(3), 289–304 (2003)

    Article  Google Scholar 

  8. Davendra, D., Zelinka, I., Senkerik, R.: Chaos driven evolutionary algorithms for the task of PID control. Computers & Mathematics with Applications 60(4), 1088–1104 (2010) ISSN 0898-1221

    Article  MathSciNet  MATH  Google Scholar 

  9. Pluhacek, M., Senkerik, R., Davendra, D., Zelinka, I.: Designing PID Controller For DC Motor System By Means Of Enhanced PSO Algorithm With Discrete Chaotic Lozi Map. In: Proceedings of the 26th European Conference on Modelling and Simulation, ECMS 2012, pp. 405–409 (2012) ISBN 978-0-9564944-4-3

    Google Scholar 

  10. Pluhacek, M., Senkerik, R., Davendra, D., Zelinka, I.: PID Controller Design For 4th Order System By Means Of Enhanced PSO algorithm With Lozi Chaotic Map. In: Proceedings of the 18th International Conference on Soft Computing, MENDEL 2012, pp. 35–39 (2012) ISBN 978-80-214-4540-6

    Google Scholar 

  11. Araujo, E., Coelho, L.: Particle swarm approaches using Lozi map chaotic sequences to fuzzy modelling of an experimental thermal-vacuum system. Applied Soft Computing 8(4), 1354–1364 (2008)

    Article  Google Scholar 

  12. Alatas, B., Akin, E., Ozer, B.A.: Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals 40(4), 1715–1734 (2009) ISSN 0960-0779

    Article  MathSciNet  MATH  Google Scholar 

  13. Pluhacek, M., Senkerik, R., Davendra, D., Kominkova Oplatkova, Z., Zelinka, I.: On the behavior and performance of chaos driven PSO algorithm with inertia weight. Computers and Mathematics with Applications (Article in press, 2013), doi:10.1016/j.camwa.2013.01.016

    Google Scholar 

  14. Pluhacek, M., Budikova, V., Senkerik, R., Oplatkova, Z., Zelinka, I.: On The Performance Of Enhanced PSO Algorithm With Lozi Chaotic Map – An initial Study. In: Proceedings of the 18th International Conference on Soft Computing, MENDEL 2012, pp. 40–45 (2012) ISBN 978-80-214-4540-6

    Google Scholar 

  15. Shi, Y.H., Eberhart, R.C.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation, Anchorage Alaska, pp. 69–73 (1998)

    Google Scholar 

  16. Nickabadi, M.M., Ebadzadeh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing 11(4), 3658–3670 (2011) ISSN 1568-4946

    Article  Google Scholar 

  17. Sprott, J.C.: Chaos and Time-Series Analysis. Oxford University Press (2003)

    Google Scholar 

  18. Aziz-Alaoui, M.A., Robert, C., Grebogi, C.: Dynamics of a Hénon–Lozi-type map. Chaos, Solitons & Fractals 12(12), 2323–2341 (2001)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Michal Pluhacek & Roman Senkerik

  2. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB-TUO, 17. listopadu 15, 708 33, Ostrava-Poruba, Czech Republic

    Ivan Zelinka & Donald Davendra

Authors
  1. Michal Pluhacek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roman Senkerik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivan Zelinka
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Donald Davendra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michal Pluhacek .

Editor information

Editors and Affiliations

  1. Faculty of Electrical Eng. & Comp. Sci, Department of Computer Science, VŠB-TUO, 17. listopadu 15, Ostrava-Poruba, 708 33, Czech Republic

    Ivan Zelinka

  2. , Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, China, People's Republic

    Guanrong Chen

  3. , Institute of Physical, University of Tuebingen, Auf der Morgenstelle 8, Tuebingen, 72076, Germany

    Otto E. Rössler

  4. Faculty of Electrical Eng. & Comp. Sci., Department of Computer Science, VŠB-TUO, 17. listopadu 15, Ostrava-Poruba, 708 33, Czech Republic

    Vaclav Snasel

  5. (MIR Labs), Scientific Network for Innovation, Machine Intelligence Research Labs, Auburn, 98071, Washington, USA

    Ajith Abraham

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Pluhacek, M., Senkerik, R., Zelinka, I., Davendra, D. (2013). New Adaptive Approach for Chaos PSO Algorithm Driven Alternately by Two Different Chaotic Maps – An Initial Study. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-00542-3_9

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00541-6

  • Online ISBN: 978-3-319-00542-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation