Concurrent Design of Heterogeneous Object Based on Method of Feasible Direction and Genetic Algorithm

  • Li Ren
  • Rui Yang
  • Dongming Guo
  • Dahai Mi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


In this paper, we propose a new approach combining the method of feasible direction (MFD) with the genetic algorithm (GA) to the concurrent design of geometry and material for the heterogeneous object. For a component made of heterogeneous materials, its functional requirements can be satisfied by changing the component’s configuration or materials’ distribution. Thus, its geometric feature and material feature are coupled with each other. For the reason of the coupling of geometry with material and the non-linearity of design problem, the conventional gradient-based algorithm is not very competent. To address this issue, the combining algorithm is used in such a way that, the increments of geometric variables can be calculated by MFD while that of material variables are obtained through GA, which implements their simultaneous optimization and solves their coupling. An isothermal heat utensil made of ZrO2 and Ni is designed and the optimization result shows that the method proposed is of good engineering applicability.


Genetic Algorithm Geometric Variable Move Little Square Simultaneous Optimization Primary Material 
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.
    Qian, X., Dutta, D.: Design of heterogeneous turbine blade. Computer-Aided Design 35, 319–329 (2003)Google Scholar
  2. 2.
    Huang, J., Fadel, G.M.: Heterogeneous flywheel modeling and optimization. Materials and Design 21, 111–125 (2000)CrossRefGoogle Scholar
  3. 3.
    Chen, K., Feng, X.: Computer-aided design method for the components made of heterogeneous materials. Computer-Aided Design 35, 453–466 (2003)CrossRefGoogle Scholar
  4. 4.
    Huang, J., Fadel, G.M.: Bi-objective optimization design of heterogeneous injection mold cooling systems. ASME Trans. 123(2), 226–239 (2001)CrossRefGoogle Scholar
  5. 5.
    Belytschko, T., Lu, Y.Y., Gu, L.: Element-free Galerkin methods [J]. Int. J. for Num. Methods in Engrg. 37, 229–256 (1994)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Kostreva, M.M., Chen, X.: A Multi-Directional Method of Feasible Directions. In: International Symposium on Computational and Optimization Algorithms, Techniques and Applications, Orlando, USA, July 12-16 (1998)Google Scholar
  7. 7.
    Yao, X.: Evolutionary Computation: Theory and Applications. World Scientific, Singapore (1999)Google Scholar
  8. 8.
    Renner, G., Ekart, A.: Genetic algorithms in computer aided design. Computer-Aided Design 35, 709–726 (2003)CrossRefGoogle Scholar
  9. 9.
    Michalewicz, Z., Dasgupta, D., Le Riche, G., et al.: Evolutionary algorithms for constrained engineering problems. Computers ind. Engng. 30(4), 851–870 (1996)CrossRefGoogle Scholar
  10. 10.
    Tan, K.C., Lim, M.H., Yao, X., Wang, L.P. (eds.): Recent Advances in Simulated Evolution And Learning. World Scientific, Singapore (2004)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Li Ren
    • 1
  • Rui Yang
    • 1
  • Dongming Guo
    • 1
  • Dahai Mi
    • 1
  1. 1.Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of EducationDalian University of TechnologyDalianP.R. China

Personalised recommendations