Skip to main content

A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design

  • Conference paper
  • First Online:
Evolutionary Multi-Criterion Optimization (EMO 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

Included in the following conference series:

Abstract

Evolutionary optimization algorithms work with a population of solutions, instead of a single solution. Since multi-objective optimization problems give rise to a set of Pareto-optimal solutions, evolutionary optimization algorithms are ideal for handling multi-objective optimization problems. Over many years of research and application studies have produced a number of efficient multi-objective evolutionary algorithms (MOEAs), which are ready to be applied to real-world problems. In this paper, we propose a practical approach, which will enable an user to move closer to the true Pareto-optimal front and simultaneously reduce the size of the obtained non-dominated solution set. The efficacy of the proposed approach is demonstrated in solving a number of mechanical shape optimization problems, including a simply-supported plate design, a cantilever plate design, a hoister design, and a bicycle frame design. The results are interesting and suggest immediate application of the proposed technique in more complex engineering design problems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Chapman, C. D., Jakiela, M. J. (1996). Genetic algorithms based structural topology design with compliance and topology simplification considerations. ASME Journal of Mechanical Design, 118, 89–98.

    Article  Google Scholar 

  2. Chapman, C. D., Saitou, K., Jakiela, M. J. (1994). Genetic algorithms as an approach to configuration and topology design. ASME Journal of Mechanical Design, 116, 1005–1012.

    Article  Google Scholar 

  3. Deb, K., Pratap, A., Agrawal, S. and Meyarivan, T. (2000). A fast and elitist multi-objective genetic algorithm: NSGA-II. Technical Report No. 2000001. Kanpur: Indian Institute of Technology Kanpur, India.

    Google Scholar 

  4. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T. (2000). A Fast Elitist Non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Proceedings of the Parallel Problem Solving from Nature VI Conference, pp. 849–858.

    Google Scholar 

  5. Duda, J. W. and Jakiela, M. J. (1997). Generation and classification of structural topologies with genetic algorithm speciation. ASME Journal of Mechanical Design, 119, 127–131.

    Article  Google Scholar 

  6. Fonseca, C. M. and Fleming, P. J. (1993). Genetic algorithms for multi-objective optimization: Formulation, discussion, and generalization. Proceedings of the Fifth International Confer-ence on Genetic Algorithms. 416–423.

    Google Scholar 

  7. Goldberg, D. E., Deb, K., and Clark, J. H. (1992). Genetic algorithms, noise, and the sizing of populations. Complex Systems, 6, 333–362.

    MATH  Google Scholar 

  8. Hamada, H. and Schoenauer, M. (2000). Adaptive techniques for evolutionary optimum de-sign. Proceedings of the Evolutionary Design and Manufacture., pp. 123–136.

    Google Scholar 

  9. Harik, G., Cantu-paz, E. Goldberg, D. E., and Miller, B. L. (1999). The gambler’s ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation, 7(3), 231–254.

    Article  Google Scholar 

  10. Horn, J. and Nafploitis, N., and Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multi-objective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation. 82–87.

    Google Scholar 

  11. Isibuchi, M. and Murata, T. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man and Cybernetics—— Part C: Applications and reviews, 28(3), 392–403.

    Article  Google Scholar 

  12. Jakiela, M. J., Chapman, C., Duda, J., Adewuya, A., abd Saitou, K. (2000). Continuum struc-tural topology design with genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186, 339–356.

    Article  MATH  MathSciNet  Google Scholar 

  13. Kim, H., Querin, O. M., and Steven, G. P. (2000). Post-processing of the two-dimensional evolutionary structural optimization topologies. In I. Parmee (Ed.) Evolutionary Design and Manufacture, London: Springer. pp. 33–44.

    Google Scholar 

  14. Knowles, J. and Corne, D. (1999) The Pareto archived evolution strategy: A new baseline algorithm for multi-objective optimization. Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway: New Jersey: IEEE Service Center, 98–105.

    Google Scholar 

  15. Sandgren, E., Jensen, E, and Welton, J. (1990). Topological design of structural components using genetic optimization methods. Proceedings of the Winter Annual Meeting of the Amer-ican Society of Mechanical Engineers, pp. 31–43.

    Google Scholar 

  16. Srinivas, N. and Deb, K. (1995). Multi-Objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation(2), 221–248.

    Google Scholar 

  17. Zitzler, E. and Thiele, L. (1998). An evolutionary algorithm for multi-objective optimiza-tion: The strength Pareto approach. Technical Report No. 43 (May 1998). Zürich: Computer Engineering and Networks Laboratory, Switzerland.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Deb, K., Goel, T. (2001). A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_27

Download citation

  • DOI: https://doi.org/10.1007/3-540-44719-9_27

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics