Advertisement

Fuzzy Logic for Parameter Tuning in Evolutionary Computation and Bio-inspired Methods

  • Fevrier Valdez
  • Patricia Melin
  • Oscar Castillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)

Abstract

We describe in this paper an approach for mathematical function optimization using fuzzy logic for parameter tuning combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). The proposed method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy logic is helpful to find the optimal parameters in PSO and GA in the best way possible. Also, with the tuning of parameters based on fuzzy logic it is possible to balance the exploration and exploitation of the proposed method. The hybrid method is called FPSO+FGA and was tested with a set of benchmark mathematical functions.

Keywords

FPSO FGA Fuzzy Logic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Designs. Springer, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  2. 2.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  3. 3.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  4. 4.
    Holland, J.H.: Adaptation in natural and artificial system. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  5. 5.
    Valdez, F., Melin, P.: Parallel Evolutionary Computing using a cluster for Mathematical Function Optimization, Nafips, San Diego, CA, USA, pp. 598–602 (June 2007)Google Scholar
  6. 6.
    Castillo, O., Melin, P.: Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory. IEEE Transactions on Neural Networks 13(6), 1395–1408 (2002)CrossRefGoogle Scholar
  7. 7.
    Fogel, D.B.: An introduction to simulated evolutionary optimization. IEEE transactions on neural networks 5(1), 3–14 (1994)CrossRefGoogle Scholar
  8. 8.
    Goldberg, D.: Genetic Algorithms. Addison-Wesley, Reading (1988)zbMATHGoogle Scholar
  9. 9.
    Emmeche, C.: Garden in the Machine. The Emerging Science of Artificial Life, p. 114. Princeton University Press, Princeton (1994)Google Scholar
  10. 10.
    Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: Proceedings 1998 IEEE World Congress on Computational Intelligence, Anchorage, Alaska, pp. 84–89. IEEE, Los Alamitos (1998)Google Scholar
  11. 11.
    Angeline, P.J.: Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 601–610. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Back, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press, Oxford (1997)zbMATHGoogle Scholar
  13. 13.
    Montiel, O., Castillo, O., Melin, P., Rodriguez, A., Sepulveda, R.: Human evolutionary model: A new approach to optimization. Inf. Sci. 177(10), 2075–2098 (2007)CrossRefGoogle Scholar
  14. 14.
    Castillo, O., Valdez, F., Melin, P.: Hierarchical Genetic Algorithms for topology optimization in fuzzy control systems. International Journal of General Systems 36(5), 575–591 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Kim, D., Hirota, K.: Vector control for loss minimization of induction motor using GA–PSO. Applied Soft Computing 8, 1692–1702 (2008)CrossRefGoogle Scholar
  16. 16.
    Liu, H., Abraham, A.: Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Future Generation Computer Systems (article in press)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fevrier Valdez
    • 1
  • Patricia Melin
    • 1
  • Oscar Castillo
    • 1
  1. 1.Computer Science in the Graduate DivisionTijuana Institute of TechnologyTijuana

Personalised recommendations