Application of genetic algorithms for optimization of tire pitch sequences

  • Yukio Nakajima
  • Akihiko Abe


A simple genetic algorithms (GAs) has been applied to generate the optimum pitch sequence. Though a simple GAs worked properly, there was the problem of the premature convergence. To solve this problem, we introduced the new operator named the growth and combined it with a simple GAs. The growth operator, which is a kind of the hill-climbing technique, has the function to get the local optimum in a small CPU time.

The GA with growth generated better sequence than a simple GAs. The GA with growth was verified not to have the premature convergence even in the smaller population size. The optimum pitch sequence generated by the GA with growth improved the noise performance such as pass-by noise compared with the current pitch sequence.

Key words

tire optimization genetic algorithm pitch sequence 


  1. [1]
    Y. Nakajima, Y. Inoue and H. Ogawa, Application of the boundary element method and modal analysis to tire acoustics problems. Tire Science and Technology,21 (1993), 66.CrossRefGoogle Scholar
  2. [2]
    JATMA, On Noise due to Tire and Road (4th version). (in Japanese).Google Scholar
  3. [3]
    J.H. Varterasian, Quieting noise mathematically — Its application to snow tires. SAE paper, No.690520, 1969.Google Scholar
  4. [4]
    Japanese Patent No.3-23366.Google Scholar
  5. [5]
    Japanese Patent No.4-232105.Google Scholar
  6. [6]
    Japanese Patent No.4-363234.Google Scholar
  7. [7]
    European Patent No.0 543 493 A1.Google Scholar
  8. [8]
    Y. Nakajima, T. Kamegawa and A. Abe, New tire design procedure based on optimization technique. SAE Technical Paper Series, 960997, 1996.Google Scholar
  9. [9]
    USA Patent No.F01613US.Google Scholar
  10. [10]
    A. Abe, T. Kamegawa and Y. Nakajima, Optimum Young’s modulus distribution in tire design. Tire Science and Technology,24 (1996), 204.CrossRefGoogle Scholar
  11. [11]
    Y. Nakajima, T. Kamegawa and A. Abe, Theory of optimum tire contour and its application. Tire Science and Technology,24 (1996), 184.CrossRefGoogle Scholar
  12. [12]
    G.N. Vanderplaats, Numerical Optimization Techniques for Engineering Design with Applications. McGraw-Hill, 1984.Google Scholar
  13. [13]
    L. Davis (ed.), Handbook of Genetic Algorithm. Van Nostrand Reinhold, 1991.Google Scholar
  14. [14]
    D.E. Goldberg, Genetic Algorithm in Search, Optimization & Machine Learning. Addison-Wesley, 1989.Google Scholar
  15. [15]
    J.H. Holland, Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975.Google Scholar
  16. [16]
    E. Sandgren and E. Jensen, Automotive structural design employing a genetic optimization algorithm. SAE Technical Paper, No.920772, 1992.Google Scholar
  17. [17]
    H. Sugimoto, Discrete optimization of truss structures and genetic algorithms. Proceedings of the Korea-Japan Joint Seminar on Structural Optimization, 1992.Google Scholar

Copyright information

© JJIAM Publishing Committee 2000

Authors and Affiliations

  • Yukio Nakajima
    • 1
  • Akihiko Abe
    • 1
  1. 1.Bridgestone CorporationTokyoJapan

Personalised recommendations