Skip to main content

Robust Design of Noise Attenuation Barriers with Evolutionary Multiobjective Algorithms and the Boundary Element Method

  • Conference paper
Evolutionary Multi-Criterion Optimization (EMO 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5467))

Included in the following conference series:

Abstract

Multiobjective shape design of acoustic attenuation barriers is handled using a boundary element method modeling and evolutionary algorithms. Noise barriers are widely used for environmental protection near population nucleus in order to reduce the noise impact. The minimization of the acoustic pressure and the minimization of the cost of the barrier -considering its total length- are taken into account. First, a single receiver point is considered; then the case of multiple receiver locations is introduced, searching for a single robust shape design where the acoustic attenuation is minimized simultaneously in different locations using probabilistic dominance relation. The case of Y-shaped barriers with upper absorbing surface is presented here. Results include a comparative between the strategy of introducing a single objective optimum in the initial multiobjective population (seeded approach) and the standard approach. The methodology is capable to provide improved robust noise barrier designs successfully.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aznárez, J.J., Greiner, D., Maeso, O., Winter, G.: A methodology for optimum design of Y-shape noise barriers. In: 19th International Congress on Acoustics (September 2007)

    Google Scholar 

  2. Basseur, M., Zitzler, E.: A preliminary study on handling uncertainty in indicator-based multiobjective optimization. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 727–739. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Baulac, M., Defrance, J., Jean, P.: Optimization of multiple edge barriers with genetic algorithms coupled with a Nelder-Mead local search. Journal of Sound and Vibration 300(1-2), 71–87 (2007)

    Article  Google Scholar 

  4. Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for solving multi-objective problems. GENA Series. Kluwer Academic Publishers, Dordrecht (2002)

    Book  MATH  Google Scholar 

  5. Coello, C.: Evolutionary Multiobjective Optimization: A Historical View of the Field. IEEE Computational Intelligence Magazine 1(1), 28–36 (2006)

    Article  Google Scholar 

  6. Crombie, D.H., Hothersall, D.C.: The performance of multiple noise barriers. Journal of Sound and Vibration 176(9), 447–459 (1994)

    MATH  Google Scholar 

  7. Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  8. Delany, M.E., Bazley, E.N.: Acoustical properties of fibrous absorbent materials. Applied Acoustics 3, 105–116 (1970)

    Article  Google Scholar 

  9. Domínguez, J.: Boundary Elements in Dynamics, Computational. Mechanics Publications: Southampton and Elsevier Applied Science, New York (1993)

    MATH  Google Scholar 

  10. Duhamel, D.: Shape optimization of noise barriers using genetic algorithms. Journal of Sound and Vibration 297, 432–443 (2006)

    Article  Google Scholar 

  11. Everson, R., Fieldsend, J.: Multiobjective Optimization of Safety Related Systems: An application to short-term conflict alert. IEEE Transactions on Evolutionary Computation 10(2), 187–198 (2006)

    Article  Google Scholar 

  12. Fonseca, C., Paquete, L., López-Ibáñez, M.: An improved dimension-sweep algorithm for the hypervolume indicator. IEEE Congress on Evolutionary Computation, 1157–1163 (2006)

    Google Scholar 

  13. Fonseca, C., Fleming, P.: On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 584–593. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  14. Greiner, D., Aznárez, J.J., Maeso, O., Winter, G.: Shape Design of Noise Barriers using Evolutionary Optimization and Boundary Elements. In: Topping, B., Montero, G., Montenegro, R. (eds.) Proceedings of the Fifth International Conference on Engineering Computational Technology, Civil-Comp Press (September 2006)

    Google Scholar 

  15. Greiner, D., Emperador, J.M., Winter, G.: Single and Multiobjective Frame Optimization by Evolutionary Algorithms and the Auto-adaptive Rebirth Operator. Computer Methods in Applied Mechanics and Engineering 193, 3711–3743 (2004)

    Article  MATH  Google Scholar 

  16. Grunert da Fonseca, V., Fonseca, C., Hall, A.: Inferential performance assessment of stochastic optimisers and the attainment function. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 213–225. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  17. Hothersall, D.C., Chandler-Wilde, S.N., Hajmirzae, M.N.: Efficiency of single noise barriers. Journal of Sound and Vibration 146(2), 303–322 (1991)

    Article  Google Scholar 

  18. Knowles, J.: A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers. IEEE Intelligent Systems Design and Applications –ISDAV (2005)

    Google Scholar 

  19. Jin, Y.: Evolutionary Optimization in uncertain environments – A survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)

    Article  Google Scholar 

  20. Limbourg, P., Kochs, H.D.: Multi-objective optimization of generalized reliability design problems using feature models – A concept for early design states. Reliability Engineering and System Safety 93, 815–828 (2008)

    Article  Google Scholar 

  21. Maeso, O., Greiner, D., Aznárez, J.J., Winter, G.: Design of noise barriers with boundary elements and genetic algorithms. In: 9th International Conference on Boundary Element Techniques (July 2008)

    Google Scholar 

  22. Maeso, O., Aznárez, J.: Strategies for reduction of acoustic impact near highways. An application of BEM. University of Las Palmas of GC (2005), http://contentdm.ulpgc.es/cdm4/item_viewer.php?CISOROOT=/DOCULPGC&CISOPTR=2389&CISOBOX=1&REC=2

  23. Seznec, R.: Diffraction of sound around barriers: use of Boundary Elements Technique. Journal of Sound and Vibration 73, 195–209 (1980)

    Article  MATH  Google Scholar 

  24. Suh, S., Mongeau, L., Bolton, J.S.: Application of the Boundary Element Method to prediction of highway noise barrier performance. Sustainability and Environmental Concerns in Transportation, Transportation Research Record, 65–74 (2002)

    Google Scholar 

  25. Teich, J.: Pareto-front exploration with uncertain objectives. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 314–328. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  26. Von Estorff, O.: Numerical methods in acoustics: facts, fears, future. Revista de Acústica 38(3-4), 83–101 (2007)

    Google Scholar 

  27. Whitley, D., Rana, S., Heckendorn, R.: Representation Issues in Neighborhood Search and Evolutionary Algorithms. In: Quagliarella, D., Périaux, J., Poloni, C., Winter, G. (eds.) Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, pp. 39–57. John Wiley & Sons, Chichester (1997)

    Google Scholar 

  28. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - A comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Greiner, D., Galván, B., Aznárez, J.J., Maeso, O., Winter, G. (2009). Robust Design of Noise Attenuation Barriers with Evolutionary Multiobjective Algorithms and the Boundary Element Method. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, JK., Sevaux, M. (eds) Evolutionary Multi-Criterion Optimization. EMO 2009. Lecture Notes in Computer Science, vol 5467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01020-0_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01020-0_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01019-4

  • Online ISBN: 978-3-642-01020-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics