Adaptive-Partitioning-Based Stochastic Optimization Algorithm and Its Application to Fuzzy Control Design

  • Chang-Wook Han
  • Jung-Il Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)


A random signal-based learning merged with simulated annealing (SARSL), which is serial algorithm, has been considered by the authors. But the serial nature of SARSL degrades its performance as the complexity of the search space is increasing. To solve this problem, this paper proposes a population structure of SARSL (PSARSL) which enables multi-point search. Moreover, adaptive partitioning method (APM) is used to reduce the optimization time. The validity of the proposed algorithm is conformed by applying it to a simple test function example and a general version of fuzzy controller design.


Simulated Annealing Fuzzy Controller Inverted Pendulum Simple Genetic Algorithm Serial Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    De Jong, K.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. dissertation, Dept. Computer Sci., Univ. Michigan, Ann Arbor, MI (1975)Google Scholar
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  3. 3.
    Han, C.W., Park, J.I.: Design of a Fuzzy Controller using Random Signal-based Learning Employing Simulated Annealing. In: Proc. of the IEEE Conference on Decision and Control, Sydney, Australia, pp. 396–397 (2000)Google Scholar
  4. 4.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Romeo, F., Sangiovanni-Vincentelli, A.: A Theoretical Framework for Simulated Annealing. Algorithmica 6, 302–345 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Sullivan, K.A., Jacobson, S.H.: A Convergence Analysis of Generalized Hill Climbing Algorithms. IEEE Trans. Automatic Control 46(8), 1288–1293 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Jeong, I.K., Lee, J.J.: Adaptive Simulated Annealing Genetic Algorithm for Control Applications. International Journal of Systems Science 27(2), 241–253 (1996)CrossRefzbMATHGoogle Scholar
  8. 8.
    Tang, Z.B.: Partitioned Random Search to Optimization. In: Proc. of the American Control Conference, San Francisco (1993)Google Scholar
  9. 9.
    Procyk, T.J., Mamdani, E.H.: A Linguistic Self-organizing Process Controller. Automatica 15(1), 15–30 (1979)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chang-Wook Han
    • 1
  • Jung-Il Park
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceYeungnam UniversityGyongbukSouth Korea

Personalised recommendations