Skip to main content

Stochastic Shape Optimisation

  • Conference paper
  • 734 Accesses

Part of the Computational Imaging and Vision book series (CIVI,volume 30)

Abstract

We present a constrained shape optimisation problem solved via metaheuristic stochastic techniques. Genetic Algorithms are briefly reviewed and their adaptation to surface topography optimisation is studied. An application to flow optimisation issues is presented.

Keywords

  • Shape
  • Topography
  • Stochastic Optimisation
  • Genetic Algorithms

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (Canada)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. D.E. Goldberg, Genetic Algorithms in Search, Optimization and machine Learning, Addison-Wesley, 1989.

    Google Scholar 

  2. R. Otten and L. van Ginneken, The Annealing Algorithm, Kluwer, Boston, 1989.

    Google Scholar 

  3. Z. Michalewicz and C. Z. Janikov, Handling constraints in Genetic Algorithms, ICGA, p. 151–157, 1991.

    Google Scholar 

  4. J.H. Holland, Adaptation in Natural and Artificial Systems, MIT Press/Bradford Books, 1992.

    Google Scholar 

  5. Marc Schoenauer and Spyros Xanthakis, Constrained GA optimization, Proceedings of the 5th International Conference on Genetic Algorithms, Urbana Champaign, 1993.

    Google Scholar 

  6. Neal R. Harvey and Stepehen Mmarshall, The use of Genetic Algorithms in morphological filter design, Signal Processing: Image Communication, n. 8, p. 55–71, 1996.

    CrossRef  Google Scholar 

  7. Neal R. Harvey and Stepehen Marshall, Grey-Scale Soft Morphological Filter Optimization by Genetic Algorithms, University of Strathclyde, Glasgow, 1997.

    Google Scholar 

  8. C. A. Caciu and E. Decencière, Etude numérique de l’écoulement 3D d’un fluide visqueux incompressible entire une plaque lisse et une plaque rugueuse, N-03/04/MM, Centre de Morphologie Mathematique, ENSMP, 2004.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2005 Springer

About this paper

Cite this paper

Caciu, C.A., Decencière, E., Jeulin, D. (2005). Stochastic Shape Optimisation. In: Ronse, C., Najman, L., Decencière, E. (eds) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, vol 30. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3443-1_30

Download citation

  • DOI: https://doi.org/10.1007/1-4020-3443-1_30

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-3442-8

  • Online ISBN: 978-1-4020-3443-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics