Skip to main content
SpringerLink
Log in

A Novel Genetic Algorithm and its Application in Fuzzy Variable Structure Control of Fuel Cell

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

A novel genetic algorithm (GA) is proposed; a ranking genetic algorithm with improved crossover operator. The whole colony is divided into several sub-colonies, and each sub-colony is called a family, which is represented by its best individual. This algorithm includes two levels of structure: the family level and the harmonizing level. The families are parallel during the process of evolution. The harmonizing level ranks all families based on their fitness values, and transports the best individual of the first-rank family to low-grade families so as to accelerate their evolution. Two levels of competition are constructed; one among individuals of a family, and the other among families. The competition within a family is accomplished by a genetic algorithm with improved crossover operator. A family's mutation probability is determined by its relative competitive power. In this way, a rapid and global convergence to the optimum goal is obtained. The GA crossover operator is improved for the case of floating point operations. The improved crossover operator can generate child individuals at random within the space of the supercube, which enhances the space searching rate and precision. Finally, the proposed novel GA is applied to the fuzzy-variable structure control (FVSC) system of a molten carbonate fuel cell (MCFC). The simulation results are satisfying.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Back, T., Hoffmeister, F., and Schwefel, H. P.: A survey of evolution strategies, in: Proc. 4th Internat. Conf. on Genetic Algorithms, 1994, pp. 2-9.

  2. Chen, C. L. and Chang, M. H.: Optimal design of fuzzy sliding-mode control: A comparative study, Fuzzy Sets and Systems 93 (1998), 37-38.

    Google Scholar 

  3. Eiben, A. E. and Aarts, E. H.: Global convergence of genetic algorithms: An infinite Markov chain analysis, in: Proc. 1st Internat. Conf. on Parallel Problem Solving from Nature, 1990, 10, pp. 100-105.

  4. Goldberg, D. E.: Real-coded genetic algorithm, virtual alphabets and blocking, Complex Systems 5 (1991), 139-167.

    Google Scholar 

  5. Hsu, L.: Smooth sliding control of uncertain systems based on a prediction error, Internat. J. Robust and Nonlinear Control 7(4) (1997), 353-372.

    Google Scholar 

  6. Kuo, T. and Hwang, S. Y.: A genetic algorithm with disruptive selection-Part B, IEEE Trans. System Man Cybernet. 26 (1996), 299-307.

    Google Scholar 

  7. Michalewicz, Z. and Janikow, C. Z.: A modified genetic algorithm for optimum control problems, Comput. Math. Appl. 23(12) (1992), 83-94.

    Google Scholar 

  8. Qi, X. and Palmieri, F.: Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space-Part I, IEEE Trans. Neural Networks (1994), 102-119.

  9. Rasheed, K.: A genetic algorithm for continuous design space search, Artificial Intelligence in Engineering 11 (1997), 295-305.

    Google Scholar 

  10. Rasheed, K. and Hirsh, H.: A genetic algorithm for continuous design space search, Artificial Intelligence in Engineering 11 (1997), 295-305.

    Google Scholar 

  11. Rudolph, G.: Convergence properties of canonical genetic algorithms, IEEE Trans. Neural Networks 5(1) (1994), 96-101.

    Google Scholar 

  12. Schraudolph, N. N. and Belew, R. K.: Dynamic parameter encoding for genetic algorithms, Machine Learning 9(6) (1992), 9-21.

    Google Scholar 

  13. Wright, A.: Genetic algorithms for real parameter optimization, in: Proc. on Foundations of Genetic Algorithms (FOGA'90), 1991, pp. 205-218.

  14. Yamamura, M., Satoh, H., and Kobayashi, S.: An analysis of crossover's effect in genetic algorithms, in: Proc. 1st IEEE Conf. on Evolutionary Computation, 1994, pp. 613-618.

  15. Yun, W. M. and Xi, Y. G.: Analysis of working principle of genetic algorithm, Control Theory and Application 13(3) (1996), 297-303.

    Google Scholar 

  16. Zhang, X. K., Fang, H., and Dai, G. Z.: Study on encoding mechanism of genetic algorithm, Information and Control 26(2) (1997), 134-139.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fuel Cell Institute, Department of Automation, Shanghai Jiaotong University, Shanghai, 200030, P.R.China

    Xing-Jin Sun, Guang-Yi Cao & Xin-Jian Zhu

Authors
  1. Xing-Jin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guang-Yi Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin-Jian Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sun, XJ., Cao, GY. & Zhu, XJ. A Novel Genetic Algorithm and its Application in Fuzzy Variable Structure Control of Fuel Cell. Journal of Intelligent and Robotic Systems 31, 299–316 (2001). https://doi.org/10.1023/A:1012091311364

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1012091311364

Search

Navigation