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Mechanical Component Design for Multiple Objectives Using Generalized Differential Evolution

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Adaptive Computing in Design and Manufacture VI

Abstract

In this paper an Evolutionary Algorithm, the Differential Evolution algorithm, and its extension for constrained multi-objective optimization are described. The described extension is tested with a set of four constrained multi-objective mechanical component design problems. Results are compared to results obtained with the elitist Non-Dominated Sorting Genetic Algorithm and the results show that the extension performs comparably to the elitist Non-Dominated Sorting Genetic Algorithm and is applicable for solving multi-objective mechanical component design problems subject to multiple constraints.

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References

  1. Zitzler, E., Thiele, L.: An evolutionary algorithm for multiobjective optimization: The strength Pareto approach. Technical Report 43, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland (1998)

    Google Scholar 

  2. Deb, K.: Multi-Objective Optimization using Evolutionary algorithms. John Wiley & Sons, Chichester, England (2001)

    MATH  Google Scholar 

  3. Pareto, V.: Cours D’Economie Politique. Libraire Droz, Geneve (1964 (the first edition in 1896))

    Google Scholar 

  4. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1998)

    Book  Google Scholar 

  5. Storn, R., Price, K. V.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, ICSI (1995) [Online] Available: ftp.icsi.berkeley.edu/pub/techreports/1995/tr-95-012.ps.gz, 15.1.2004.

    Google Scholar 

  6. Storn, R., Price, K. V.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11 (1997) 341–359

    Article  MathSciNet  MATH  Google Scholar 

  7. Corne, D., Dorigo, M., Glover, F.: New Ideas in Optimization. McGraw-Hill, London (1999)

    Google Scholar 

  8. Madavan, N. K.: Multiobjective optimization using a Pareto differential evolution approach. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC’02, Honolulu, Hawaii (2002) 1145–1150

    Google Scholar 

  9. Abbass, H. A., Sarker, R.: The Pareto differential evolution algorithm. International Journal on Artificial Intelligence Tools 11 (2002) 531–552

    Article  Google Scholar 

  10. Abbass, H. A.: The self-adaptive Pareto differential evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC’02, Honolulu, Hawaii (2002) 831–836

    Google Scholar 

  11. Chang, C., Xu, D., Quek, H.: Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system. IEE Proceedings on Electric Power Applications 146 (1999) 577–583

    Article  Google Scholar 

  12. Storn, R.: System design by constraint adaptation and differential evolution. IEEE Transactions on Evolutionary Computation 3 (1999) 22–34

    Article  Google Scholar 

  13. Wang, F. S., Chiou, J. P.: Differential evolution for dynamic optimization of differential-algebraic systems. In: Proceedings of the IEEE International Conference on Evolutionary Computation, ICEC’97, Indianapolis, IN (1997) 531–536

    Google Scholar 

  14. Chang, T. T., Chang, H. C.: Application of differential evolution to passive shunt harmonic filter planning. In: 8th International Conference On Harmonics and Quality of Power, Athens, Greece (1998) 149–153

    Google Scholar 

  15. Wang, F. S., Sheu, J. W.: Multiobjective parameter estimation problems of fer-mentation processes using a high ethanol tolerance yeast. Chemical Engineering Science 55 (2000) 3685–3695

    Article  Google Scholar 

  16. Lampinen, J.: A constraint handling approach for the differential evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC’02, Honolulu, Hawaii (2002) 1468–1473

    Google Scholar 

  17. Lampinen, J.: DE’s selection rule for multiobjective optimization. Technical report, Lappeenranta University of Technology, Department of Information Technology (2001) [Online] Available: http://www.it.lut.fi/kurssit/03-04/010778000/MODE.pdf, 15.1.2004.

    Google Scholar 

  18. Lampinen, J.: Multi-constrained nonlinear optimization by the differential evolution algorithm. Technical report, Lappeenranta University of Technology, Department of Information Technology (2001) [Online] Available: http://www.it.lut.fi/kurssit/03-04/010778000/DECONSTR.PDF, 15.1.2004.

    Google Scholar 

  19. Deb, K.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186 (2000) 311–338

    Article  MATH  Google Scholar 

  20. Deb, K., Pratap, A., Moitra, S.: Mechanical component design for multiple objectives using elitist non-dominated sorting GA. In: Proceedings of the Parallel Problem Solving from Nature VI (PPSN-VI). (2000) 859–868 Also available:http://www.iitk.ac.in/kangal/papers/tech-rep2.ps, 15.1.2004.

    Google Scholar 

  21. Palli, N., Azram, S., McCluskey, P., Sundararajan, R.: An interactive multistage epsilon-inequality constraint method for multiobjective optimization. ASME Journal of Mechanical Design 120 (1999) 678–686

    Article  Google Scholar 

  22. Miettinen, K., Mäkelä, M. M., Neittaanmäki, P., Périaux, J.: Evolutionary Algorithms in Engineering and Computer Science. John Wiley & Sons, Ltd (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Technology, Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland

    Saku Kukkonen & Jouni Lampinen

Authors
  1. Saku Kukkonen
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  2. Jouni Lampinen
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Editor information

Editors and Affiliations

  1. CEMS, University of the West of England, Coldharbour Lane, Bristol, BS16 1QY, UK

    I. C. Parmee PhD, CEng, MASCE, MEI

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© 2004 Springer-Verlag London

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Kukkonen, S., Lampinen, J. (2004). Mechanical Component Design for Multiple Objectives Using Generalized Differential Evolution. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture VI. Springer, London. https://doi.org/10.1007/978-0-85729-338-1_22

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  • DOI: https://doi.org/10.1007/978-0-85729-338-1_22

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-829-9

  • Online ISBN: 978-0-85729-338-1

  • eBook Packages: Springer Book Archive

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