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Bio-inspired Optimization Methods on Graphic Processing Unit for Minimization of Complex Mathematical Functions

Part of the Studies in Computational Intelligence book series (SCI, volume 451)

Abstract

Although GPUs have been traditionally used only for computer graphics, a recent technique called GPGPU (General-purpose computing on graphics processing units) allows the GPUs to perform numerical computations usually handled by CPU. The advantage of using GPUs for general purpose computation is the performance speed up that can be achieved due to the parallel architecture of these devices. This paper describes the use of Bio-Inspired Optimization Methods as Particle Swarm Optimization and Genetic Algorithms on GPUs to demonstrate the performance that can be achieved using this technology with regard to use CPU primarily.

Keywords

PSO GA GPU CPU Optimization 

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References

  1. 1.
    Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Designs. Springer (1999)Google 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. In: 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 (1988)Google Scholar
  9. 9.
    Emmeche, C.: Garden in the Machine. The Emerging Science of Artificial Life, p. 114. Princeton University Press (1994)Google Scholar
  10. 10.
    Valdez, F., Melin, P.: Parallel Evolutionary Computing using a cluster for Mathematical Function Optimization. In: NAFIPS, San Diego CA, USA, pp. 598–602 (June 2007)Google Scholar
  11. 11.
    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 (1998)Google Scholar
  12. 12.
    Back, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press (1997)Google 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)MathSciNetMATHCrossRefGoogle 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 pressGoogle Scholar
  17. 17.
    Mohammed, O., Ali, S., Koh, P., Chong, K.: Design a PID Controller of BLDC Motor by Using Hybrid Genetic-Immune. Modern Applied Science 5(1) (February 2011)Google Scholar
  18. 18.
    Kirkpatrick, S., Gelatt, C.J., Vecchi, M.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Valdez, F., Melin, P., Castillo, O.: An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms. Appl. Soft Comput. 11(2), 2625–2632 (2011)CrossRefGoogle Scholar
  20. 20.
    Hooke, R., Jeeves, T.A.: Direct search’ solution of numerical and statistical problems. Journal of the Association for Computing Machinery (ACM) 8(2), 212–229 (1961)MATHCrossRefGoogle Scholar
  21. 21.
    Davidon, W.C.: Variable metric method for minimization. SIAM Journal on Optimization 1(1), 1–17 (1991)MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Sanders, J., Kandrot, E.: CUDA BY EXAMPLE An Introduction to General-Purpose GPU Programming. Addisson Wesley (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fevrier Valdez
    • 1
  • Patricia Melin
    • 1
  • Oscar Castillo
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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