Skip to main content
SpringerLink
Log in

Evolutionary Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

  • Chapter
Computational Optimization, Methods and Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 356))

Abstract

Optimization problems in many industrial applications are very hard to solve. Many examples of them can be found in the design of aeronautical systems. In this field, the designer is frequently faced with the problem of considering not only a single design objective, but several of them, i.e., the designer needs to solve a Multi-Objective Optimization Problem (MOP). In aeronautical systems design, aerodynamics plays a key role in aircraft design, as well as in the design of propulsion system components, such as turbine engines. Thus, aerodynamic shape optimization is a crucial task, and has been extensively studied and developed. Multi-Objective Evolutionary Algorithms (MOEAs) have gained popularity in recent years as optimization methods in this area, mainly because of their simplicity, their ease of use and their suitability to be coupled to specialized numerical simulation tools. In this chapter, we will review some of the most relevant research on the use of MOEAs to solve multi-objective and/or multi-disciplinary aerodynamic shape optimization problems. In this review, we will highlight some of the benefits and drawbacks of the use of MOEAs, as compared to traditional design optimization methods. In the second part of the chapter, we will present a case study on the application of MOEAs for the solution of a multi-objective aerodynamic shape optimization problem.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, M.B.: Genetic Algorithm. In: Aerospace Design: Substantial Progress, Tremendous Potential. Technical report, Sverdrup Technology Inc./TEAS Group, 260 Eglin Air Force Base, FL 32542, USA (2002)

    Google Scholar 

  2. Arabnia, M., Ghaly, W.: A Strategy for Multi-Objective Shape optimization of Turbine Stages in Three-Dimensional Flow. In: 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria, British Columbia Canada, September 10 –12 (2008)

    Google Scholar 

  3. AriasMontano, A., Coello, C.A.C., Mezura-Montes, E.: MODE-LD+SS: A Novel Differential Evolution Algorithm Incorporating Local Dominance and Scalar Selection Mechanisms for Multi-Objective Optimization. In: 2010 IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, IEEE Press, Los Alamitos (2010)

    Google Scholar 

  4. Benini, E.: Three-Dimensional Multi-Objecive Design optimization of a ransonic Compressor Rotor. Journal of Propulsin and Power 20(3), 559–565 (2004)

    Article  Google Scholar 

  5. Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective SelectionBasd on Dominated Hypervolume. European Journal of Operational Research 181, 1653–1659 (2007)

    Article  MATH  Google Scholar 

  6. Chiba, K., Jeong, S., Obayashi, S., Yamamoto, K.: Knowledge Discovery in Aerodynamic Design Space for Flyback–Booster Wing Using Data Mining. In: 14th AIAA/AHI Space Planes and Hypersonic System and Technologies Conference, Canberra, Australia, November 6–9 (2006)

    Google Scholar 

  7. Chiba, K., Obayashi, S., Nakahashi, K.: Design Exploration of Aerodynamic Wing Shape for Reusable Launch Vehicle Flyback Booster. Journal of Aircraft 43(3), 832–836 (2006)

    Article  MATH  Google Scholar 

  8. Chiba, K., Oyama, A., Obayashi, S., Nakahashi, K., Morino, H.: Multidisciplinary Design Optimization and Data Mining for Transonic Regional-Jet Wing. AIAA Journal of Aircraft 44(4), 1100–1112 (2007), doi:10.2514/1.17549

    Article  Google Scholar 

  9. Chung, H.-S., Choi, S., Alonso, J.J.: Supersonic Business Jet Design using a Knowledge-Based Genetic Algorithm with an Adaptive, Unstructured Grid Methodology. In: AIAA Paper 2003-3791, 21st Applied Aerodynamics Conference, Orlando, Florida, USA, June 23-26 (2003)

    Google Scholar 

  10. Coello, C.A.C.: Theoretical and Numerical Constraint Handling Techniques used with Evolutionary Algorithms: A Survey of the State of the Art. Computer Methods in Applied Mechanics and Engineering 191(11-12), 1245–1287 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  11. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001) ISBN 0-471-87339-X

    MATH  Google Scholar 

  12. Deb, K., Pratap, A., Agarwal, 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 

  13. Drela, M.: XFOIL: An Analysis and Design System for Low Reynolds Number Aerodynamics. In: Conference on Low Reynolds Number Aerodynamics. University of Notre Dame, IN (1989)

    Google Scholar 

  14. Fonseca, C.M., Fleming, P.J.: Genetic Algorithms forMultiobjective Optimization: Formulation, Discussion and Generalization. In: Stephanie, F. (ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423. University of Illinois at Urbana-Champaign, Morgan Kauffman Publishers, San Mateo, California (1993)

    Google Scholar 

  15. Gonzalez, L.F.: Robust Evolutionary Methods forMulti-objective and Multidisciplinary Design Optimization in Aeronautics. PhD thesis, School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Australia (2005)

    Google Scholar 

  16. Hua, J., Kong, F., Liu, P.y., Zingg, D.: Optimization of Long-Endurance Airfoils. In: AIAA-2003-3500, 21st AIAA Applied Aerodynamics Conference, Orlando, FL, June 23-26 (2003)

    Google Scholar 

  17. Jameson, A., Caughey, D.A., Newman, P.A., Davis, R.M.: NYU Transonic Swept-Wing Computer Program - FLO22. Technical report, Langley Research Center (1975)

    Google Scholar 

  18. Jeong, S., Chiba, K., Obayashi, S.: DataMining for erodynamic Design Space. In: AIAA Paper 2005–5079, 23rd AIAA Applied Aerodynamic Conference, Toronto Ontario Canada, June 6–9 (2005)

    Google Scholar 

  19. Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing 9(1), 3–12 (2005)

    Article  Google Scholar 

  20. Kroo, I.: Multidisciplinary Optimization Applications in Preliminary Design – Status and Directions. In: 38th, and AIAA/ASME/AHS Adaptive Structures Forum, Kissimmee, FL, April 7-10 (1997)

    Google Scholar 

  21. Kroo, I.: Innovations in Aeronautics. In: 42nd AIAA Aerospace Sciences Meeting, Reno, NV, January 5-8 (2004)

    Google Scholar 

  22. Kuhn, T., Rösler, C., Baier, H.: Multidisciplinary Design Methods for the Hybrid Universal Ground Observing Airship (HUGO). In: AIAA Paper 2007–7781, Belfast, Northern Ireland, September 18-20 (2007)

    Google Scholar 

  23. Lee, D.S., Gonzalez, L.F., Srinivas, K., Auld, D.J., Wong, K.C.: Erodynamics/RCS Shape Optimisation of Unmanned Aerial Vehicles using Hierarchical Asynchronous Parallel Evolutionary Algorithms. In: AIAA Paper 2006-3331, 24th AIAA Applied Aerodynamics Conference, San Francisco, California, USA, June 5-8 (2006)

    Google Scholar 

  24. Lee, D.S., Gonzalez, L.F., Periaux, J., Srinivas, K.: Robust Design Optimisation Using Multi-Objective Evolutionary Algorithms. Computer & Fluids 37, 565–583 (2008)

    Article  Google Scholar 

  25. Leifsson, L., Koziel, S.: Multi-fidelity design optimization of transonic airfoils using physics-based surrogate modeling and shape-preserving response prediction. Journal of Computational Science, 98–106 (2010)

    Google Scholar 

  26. Lian, Y., Liou, M.-S.: Multiobjective Optimization Using Coupled Response Surface Model end Evolutinary Algorithm. In: AIAA Paper 2004–4323, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, , Albany, New York, USA, August 30-September 1 (2004)

    Google Scholar 

  27. Lian, Y., Liou, M.-S.: Multi-Objective Optimization of Transonic Compressor Blade Using Evolutionary Algorithm. Journal of Propulsion and Power 21(6), 979–987 (2005)

    Article  Google Scholar 

  28. Liao, W., Tsai, H.M.: Aerodynamic Design optimization by the Adjoint Equation Method on Overset Grids. In: AIAA Paper 2006-54, 44th AIAA Aerospace Science Meeting and Exhibit, Reno, Nevada, USA, January 9-12 (2006)

    Google Scholar 

  29. Mezura-Montes, E. (ed.): Constraint-Handling in Evolutionary Optimization. SCI, vol. 198. Springer, Heidelberg (2009)

    Google Scholar 

  30. Mialon, B., Fol, T., Bonnaud, C.: Aerodynamic Optimization Of Subsonic Flying Wing Configurations. In: AIAA-2002-2931, 20th AIAA Applied Aerodynamics Conference, St. Louis Missouri, June 24-26 (2002)

    Google Scholar 

  31. Obayashi, S., Tsukahara, T.: Comparison of OptimizationAlgorithms for Aerodynamic Shape Design. In: AIAA-96-2394-CP, AIAA 14th Applied Aerodynamics Conference, New Orleans, LA, USA, June 17-20 (1996)

    Google Scholar 

  32. Ong, Y.-S., Nair, P.B., Lum, K.Y.: Max-min surrogate-assisted evolutionary algorithm for robust design. IEEE Trans. Evolutionary Computation 10(4), 392–404 (2006)

    Article  Google Scholar 

  33. Oyama, A.: Wing Design Using Evolutionary Algorithms. PhD thesis, Department of Aeronautics and Space Engineering. Tohoku University, Sendai, Japan (March 2000)

    Google Scholar 

  34. Oyama, A., Nonomura, T., Fujii, K.: Data Mining of Pareto-Optimal Transonic Airfoil Shapes Using Proper Orthogonal Decomposition. AIAA Journal Of Aircraft 47(5), 1756–1762 (2010)

    Article  Google Scholar 

  35. Oyama, A., Okabe, Y., Shimoyama, K., Fujii, K.: Aerodynamic Multiobjective Design Exploration of a Flapping Airfoil Using a Navier-Stokes Solver. Journal Of Aerospace Computing, Information, and Communication 6(3), 256–270 (2009)

    Google Scholar 

  36. Price, K.V., Storn, R., Lampinen, J.A.: Differential Evolution. A Practical Approach to Global Optimization. Springer, Berlin (2005)

    MATH  Google Scholar 

  37. Rai, M.M.: Robust Optimal Design With Differential Evolution. In: AIAA Paper 2004- 4588, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, USA, August 30 - September 1 (2004)

    Google Scholar 

  38. Ray, T., Tsai, H.M.: A Parallel Hybrid Optimization Algorithm for Robust Airfoil Design. In: AIAA Paper 2004–905, 42nd AIAA Aerospace Science Meeting and Exhibit, Reno, Nevada, USA, January 5 -8 (2004)

    Google Scholar 

  39. Sierra, M.R., Coello, C.A.C.: A Study of Fitness Inheritance and ApproximationTechniques for Multi-Objective Particle Swarm Optimization. In: 2005 IEEE Congress on Evolutionary Computation (CEC 2005), vol. 1, pp. 65–72. IEEE Service Center, Edinburgh (2005)

    Chapter  Google Scholar 

  40. Sasaki, D., Obayashi, S.: Efficient search for trade-offs by adaptive range multiobjective genetic algorithm. Journal Of Aerospace Computing, Information, and Communication 2(1), 44–64 (2005)

    Article  Google Scholar 

  41. Sasaki, D., Obayashi, S., Nakahashi, K.: Navier-Stokes Optimization of Supersonic Wings with Four Objectives Using Evolutionary Algorithms. Journal Of Aircraft 39(4), 621–629 (2002)

    Article  Google Scholar 

  42. Secanell, M., Suleman, A.: Numerical Evaluation of Optimization Algorithms for Low-Reynolds Number Aerodynamic Shape Optimization. AIAA Journal 10, 2262–2267 (2005)

    Article  Google Scholar 

  43. Shimoyama, K., Oyama, A., Fujii, K.: A New Efficient and Useful Robust Optimization Approach –Design forMulti-objective Six Sigma. In: 2005 IEEE Congress on Evolutionary Computation (CEC 2005), vol. 1, pp. 950–957. IEEE Service Center, Edinburgh (2005)

    Chapter  Google Scholar 

  44. Shimoyama, K., Oyama, A., Fujii, K.: Development of Multi-Objective Six-Sigma Approach for Robust Design Optimization. Journal of Aerospace Computing, Information, and Communication 5(8), 215–233 (2008)

    Google Scholar 

  45. Sobieczky, H.: Parametric Airfoils and Wings. In: Fuji, K., Dulikravich, G.S. (eds.) Notes on Numerical Fluid Mechanics, vol. 68, pp. 71–88. Vieweg Verlag, Wiesbaden (1998)

    Google Scholar 

  46. Song, W., Keane, A.J.: Surrogate-based aerodynamic shape optimization of a civil aircraft engine nacelle. AIAA Journal 45(10), 265–2574 (2007), doi:10.2514/1.30015

    Article  Google Scholar 

  47. Srinivas, N., Deb, K.: Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation 2(3), 221–248 (1994)

    Article  Google Scholar 

  48. Szöllös, A., Smíd, M., Hájek, J.: Aerodynamic optimization via multiobjective micro-genetic algorithm with range adaptation, knowledge-based reinitialization, crowding and epsilon-dominance. Advances in Engineering Software 40(6), 419–430 (2009)

    Article  MATH  Google Scholar 

  49. Tani, N., Oyama, A., Okita, K., Yamanishi, N.: Feasibility study of multi objective shape optimization for rocket engine turbopump blade design. In: 44th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, Hartford, CT, July 21 - 23 (2008)

    Google Scholar 

  50. Tsutsui, S., Ghosh, A.: Genetic algorithms with a robust solution searching scheme. IEEE Trans. Evolutionary Computation 1(3), 201–208 (1997)

    Article  Google Scholar 

  51. Yamaguchi, Y., Arima, T.: Multi-Objective Optimization for the Transonic Compressor Stator Blade. In: AIAA Paper 2000–4909, 8th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, AIAA Paper 2000–4909, 8th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, September 6 - 8, Long Beach, CA, USA (2000)

    Google Scholar 

  52. Zhang, Q., Li, H.: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation 11(6), 712–731 (2007)

    Article  Google Scholar 

  53. Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), Av. IPN No. 2508, Col. San Pedro Zacatenco, México D.F., 07360, Mexico

    Alfredo Arias-Montaño & Carlos A. Coello Coello

  2. UMI LAFMIA 3175 CNRS at CINVESTAV-IPN, Mexico

    Carlos A. Coello Coello

  3. Laboratorio Nacional de Informática Avanzada (LANIA A.C.), Rébsamen 80, Centro, Xalapa, Veracruz, 91000, Mexico

    Efrén Mezura-Montes

Authors
  1. Alfredo Arias-Montaño
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos A. Coello Coello
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Efrén Mezura-Montes
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Science and Engineering Engineering Optimization & Modeling Center, Reykjavik University, Menntavegur 1, 101, Reykjavik, Iceland

    Slawomir Koziel

  2. Mathematics and Scientific Computing, National Physical Laboratory, TW11 0LW, Teddington, UK

    Xin-She Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Arias-Montaño, A., Coello Coello, C.A., Mezura-Montes, E. (2011). Evolutionary Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization. In: Koziel, S., Yang, XS. (eds) Computational Optimization, Methods and Algorithms. Studies in Computational Intelligence, vol 356. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20859-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20859-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20858-4

  • Online ISBN: 978-3-642-20859-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation