In this paper we report about deployment of intelligent optimisation algorithms for noice reduction in tire manufacturing. Since the complexity of the problem grows exponentially (the search space is typically of the order of a 65-dimensional vector space), a complete search for the optimal tread profile is not possible even with today’s computers. Thus heuristic optimization algorithms such as Genetic Algorithms and Simulated Annealing are an appropriate means to find (near) optimal tread profiles. We discuss approaches of speeding up the generation and analysis of tread profiles, and results using various optimization algorithms.


Simulated Annealing Noise Model Goal Function Complete Search Vector Size 
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.
  2. 2.
    Beckenbauer, T.: Reifen-Fahrbahn-Geräusche – Minderungspotentiale der Strassenoberfläche. In: Proceedings of the Deutsche Arbeitsgemeinschaft für Akustik (DAGA 2003), Aachen, Germany (2003)Google Scholar
  3. 3.
    Briggs, W., Henson, V.E.: The DFT. SIAM, Philadelphia (1995)MATHGoogle Scholar
  4. 4.
    Michalewicz, Z., Fogel, D.B.: How to solve it: Modern Heuristics. Springer, London (1999)Google Scholar
  5. 5.
    Szczerbicka, H., Syrjakow, M., Becker, M.: Genetic algorithms, a tool for modelling, simulation and optimization of complex systems. Cybernetics and Systems: An International Journal, Special Issue: Intelligent modelling and simulation for complex systems II(7), 639–660 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Matthias Becker
    • 1
  • Helena Szczerbicka
    • 1
  1. 1.University HannoverHannoverGermany

Personalised recommendations