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Multi-criteria Airfoil Design with Evolution Strategies

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Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

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Abstract

In this paper we will describe the optimisation of a two-criteria wing-design problem where calculation of the objective function requires the solution of the two-dimensional Navier-Stokes equations. It will be shown that basic concepts of the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Non dominated Sorting Genetic Algorithm II (NSGA-II) work well with Evolution Strategies. Results for the wing design problem are presented for the selection operators of SPEA2 and NSGA-II in combination with three different mutation operators. These results are compared with results found by a multi-objective Genetic Algorithm.

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References

  1. Kalyanmoy Deb. Multi-Objective Optimization using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization. John Wiley & Sons, Chichester, New York, 2001.

    Google Scholar 

  2. Kalyanmoy Deb and Tushar Goel. Controlled elitist non-dominated sorting genetic algorithms for better convergence. In Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO-2001), pages 67–81, 7–9 March 2001.

    Google Scholar 

  3. Nikolaus Hansen, Andreas Ostermeier, and Andreas Gawelcyk. A derandomized approach to self-adaptation of evolution strategies. Evolutionary Computation, 4(2):369–380, 1994.

    Google Scholar 

  4. Marco Laumanns, Günther Rudolph, and Hans Paul Schwefel. Mutation control and convergence in evolutionary multi-objective optimization. In Proceedings of the 7th International Mendel Conference on Soft Computing (MENDEL 2001), Brno, Czech Republic, June 2001.-.

    Google Scholar 

  5. Marco Laumanns, Eckart Zitzler, and Lothar Thiele. On the effects of archiving, elitism, and density based selection in evolutionary multi-objective optimization. In E. Zitzler et al., editor, Evolutionary Multi-criterion Optimization (EMO 2001), First International Conference, Proceedings. Lecture Notes on Computer Science, pages 181–196. Springer, March 7–9 2001.

    Google Scholar 

  6. Marco Laumanns, Eckart Zitzler, and Lothar Thiele. Spea2: Improving the strength pareto evolutionary algorithm. TIK-Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, May 2001.-.

    Google Scholar 

  7. Boris Naujoks, Lars Willmes, Werner Haase, Thomas Bäck, and Martin Schütz. Multi-point airfoil optimization using evolution strategies. In Proceedings of the European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS’00) (CD-Rom und Book of Abstracts), page 948 (Book of Abstracts), Barcelona, 11.–14. September 2000. Center for Numerical Methods in Engineering (CIMNE).

    Google Scholar 

  8. Carlo Poloni. Multi objective aerodynamic optimisation by means of robust and efficient genetic algorithm. In Kozo Fujii and George S. Dulikravich, editors, Recent development of aerodynamic design methodologies: inverse design and optimization, volume 68 of Notes on numerical fluid mechanics, pages 1–24. Vieweg, Braunschweig/Wiesbaden, 1999.

    Google Scholar 

  9. Domenico Quagliarella, Jaques Périaux, Carlo Polnoi, and Gabriel Winter, editors. Genetic Algorithms and Evolution Strategies in Engineering and Computer Science — Recent Advances and Industrial Applications. John Wiley & Sons, Chichester, New York, 1998.

    MATH  Google Scholar 

  10. M. Schütz and J. Sprave. Application of parallel mixed-integer evolution strategies with mutation rate pooling. In L.J. Fogel, P.J. Angeline, and T. Bäck, editors, Proceedings of the 5th Annual Conference on Evolutionary Programming (EP-96), San Diego, CA, 29. February — 2. March, pages 345–354, 1996.

    Google Scholar 

  11. Hans Paul Schwefel. Evolution and Optimum Seeking. Wiley, New York, 1995.

    Google Scholar 

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Author information

Authors and Affiliations

  1. NuTech Solutions GmbH, Martin-Schmeißer-Weg 15, 44227, Dortmund, Germany

    Lars Willmes & Thomas Bäck

  2. Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Niels-Bohrweg 1, 2333 CA, Leiden, The Netherlands

    Thomas Bäck

Authors
  1. Lars Willmes
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  2. Thomas Bäck
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Gloriastraße 35, 8092, Zürich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  4. Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, UP, 208 016, India

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Willmes, L., Bäck, T. (2003). Multi-criteria Airfoil Design with Evolution Strategies. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_55

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  • DOI: https://doi.org/10.1007/3-540-36970-8_55

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

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