Advertisement

Multi-chaotic Approach for Particle Acceleration in PSO

  • Michal PluhacekEmail author
  • Roman Senkerik
  • Adam Viktorin
  • Ivan Zelinka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9668)

Abstract

This paper deals with novel approach for hybridization of two scientific techniques: the evolutionary computational techniques and deterministic chaos. The Particle Swarm Optimization algorithm is enhanced with two pseudo-random number generators based on chaotic systems. The chaotic pseudo-random number generators (CPRNGs) are used to guide the particles movement through multiplying the accelerating constants. Different CPRNGs are used simultaneously in order to improve the performance of the algorithm. The IEEE CEC’13 benchmark suite is used to test the performance of the proposed method.

Keywords

Particle swarm optimization PSO Chaos Acceleration constant 

Notes

Acknowledgements

This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014). Also by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Project no. IGA/Ceb-iaTech/2016/007.

References

  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Shi, Y.H., Eberhart, R.C.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation, pp. 69–73, Anchorage Alaska (1998)Google Scholar
  3. 3.
    Nickabadi, M., Ebadzadeh, M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11(4), 3658–3670 (2011). ISSN 1568-4946CrossRefGoogle Scholar
  4. 4.
    Eberhart, R., Kennedy, J.: Swarm Intelligence. The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, Los Altos (2001)Google Scholar
  5. 5.
    Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.G.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans. Evol. Comput. 7(3), 289–304 (2003)CrossRefGoogle Scholar
  6. 6.
    Davendra, D., Zelinka, I., Senkerik, R.: Chaos driven evolutionary algorithms for the task of PID control. Comput. Math Appl. 60(4), 1088–1104 (2010). ISSn 0898-1221MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Alatas, B., Akin, E., Ozer, B.A.: Chaos embedded particle swarm optimization algorithms. Chaos, Solitons Fractals 40(4), 1715–1734 (2009). ISSN 0960-0779MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    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 978-0-9564944-4-3Google Scholar
  9. 9.
    Araujo, E., Coelho, L.: Particle swarm approaches using Lozi map chaotic sequences to fuzzy modelling of an experimental thermal-vacuum system. Appl. Soft Comput. 8(4), 1354–1364 (2008)CrossRefGoogle Scholar
  10. 10.
    Pluhacek, M., Senkerik, R., Davendra, D., Zelinka, I.: Particle swarm optimization algorithm driven by multichaotic number generator. Soft. Comput. 18(4), 631–639 (2014)CrossRefGoogle Scholar
  11. 11.
    Sprott, J.C.: Chaos and Time-Series Analysis. Oxford University Press, Oxford (2003)zbMATHGoogle Scholar
  12. 12.
    Liang, J.J., Qu, B.-Y., Suganthan, P.N., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session and competition on real-parameter optimization. Technical report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical report, Nanyang Technological University, Singapore (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Michal Pluhacek
    • 1
    Email author
  • Roman Senkerik
    • 1
  • Adam Viktorin
    • 1
  • Ivan Zelinka
    • 2
  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic
  2. 2.Faculty of Electrical Engineering and Computer ScienceTechnical University of OstravaOstrava-PorubaCzech Republic

Personalised recommendations