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High-Fidelity Models in Global Optimization

  • Conference paper
Global Optimization and Constraint Satisfaction (COCOS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3478))

Abstract

This work presents a Simulation Based Design environment based on a Global Optimization (GO) algorithm for the solution of optimum design problems. The procedure, illustrated in the framework of a multiobjective ship design optimization problem, make use of high-fidelity, CPU time expensive computational models, including a free surface capturing RANSE solver. The use of GO prevents the optimizer to be trapped into local minima.

The optimization is composed by global and local phases. In the global stage of the search, a few computationally expensive simulations are needed for creating surrogate models (metamodels) of the objective functions. Tentative design, created to explore the design variable space are evaluated with these inexpensive analytical approximations. The more promising designs are clustered, then locally minimized and eventually verified with high-fidelity simulations. New exact values are used to improve the metamodels and repeated cycles of the algorithm are performed. A Decision Maker strategy is finally adopted to select the more promising design.

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References

  1. Barthelemy, J.-F.M., Haftka, R.T.: Approximation concepts for optimum structural design - a review. Structural Optim. 5, 129–144 (1993)

    Article  Google Scholar 

  2. Bassanini P., Bulgarelli U., Campana E.F., Lalli F.: The wave resistance problem in a boundary integral formulation. Surv. Math. Ind. (April 1994)

    Google Scholar 

  3. Becker, R.W., Lago, G.V.: A global optimization algorithm. In: Proceedings of the 8th Allerton Conference on Circuits and Systems Theory, pp. 3–12 (1970)

    Google Scholar 

  4. Chang, K.J., Haftka, R.T., Giles, G.L., Kao, P.-J.: Sensitivity-based scaling for approximating structural response. Journal of Aircraft 30(2), 283–288 (1993)

    Article  Google Scholar 

  5. Cheng, B., Titterington, D.M.: Neural networks: a review from a statistical perspective. Statistical Sci. 9i, 2–54 (1994)

    Article  MathSciNet  Google Scholar 

  6. Di Mascio, A., Broglia, R., Favini, B.: A second-order Godunov-type scheme for Naval Hydrodynamics. In: Godunov (ed.) Methods: Theory and application, Kluwer Academic/Plenum Singapore. (2000)

    Google Scholar 

  7. Dixon, L.C.W., Szegö, G.P.: Towards global optimization. North-Holland, Amsterdam (1975)

    Google Scholar 

  8. Giering, R., Kamnski, T.: Recipes for Adjoint Code Construction. ACM Trans. on Math. Software 24(4), 437–474 (1998)

    Article  MATH  Google Scholar 

  9. Giunta, A.A., Balabanov, V., Kaufman, M., Burgee, S., Grossman, B., Haftka, R.T., Mason, W.H., Watson, L.T.: Variable-Complexity Response Surface Design of an HSCT configuration. In: Alexandrov, N.M., Hussaini, M.Y. (eds.) Multidisciplinary Design Optimization. SIAM, Philadelphia (1997)

    Google Scholar 

  10. Haftka, R.T., Vitali, R., Sankar, B.: Optimization of Composite Structures Using Response Surface Approximations. NATO ASI meeting on Mechanics of Composite Materials and Structures, Troia, Portugal (1998)

    Google Scholar 

  11. Haimes, Y.Y., Li, D.: Hierarchical multiobjective analysis for large-scale systems: review and current status. Automatica 24(1), 53–69 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  12. Hedayat, A.S., Sloane, N.J.A., Stufken, J.: Orthogonal Arrays: Theory and Applications. Springer Series in Statistics. Springer, Berlin (1999)

    MATH  Google Scholar 

  13. Hino, T., Kodama, Y., Hirata, N.: Hydrodynamic shape optimization of ship hull forms using CFD. Third Osaka Colloquium on Advanced CFD Applications to Ship Flow and Hull Form Design. Osaka Prefecture Univ. and Osaka Univ, Japan (1998)

    Google Scholar 

  14. Iwashita, H., Nechita, M., Colagrossi, A., Landrini, M., Bertram, V.: A Critical Assessment of Potential Flow Models for Ship Seakeeping, pp. 37–64. Osaka Colloquium on Seakeeping, Osaka (2000)

    Google Scholar 

  15. Jin, R., Chen, W., Simpson, T.W.: Comparative studies of metamodelling techniques under multiple modelling criteria. Struct. Multidisc. Optim. 23 (2001)

    Google Scholar 

  16. Knill, D.L., Giunta, A.A., Baker, C.A., Grossman, B., Mason, W.H., Haftka, R.T., Watson, L.T.: Response surface models combining linear and Euler aerodynamics for supersonic transport design. Journal of Aircraft 36(1), 75–86 (1999)

    Article  Google Scholar 

  17. Minami, Y., Hinatsu M.: Multi Objective Optimization of Ship Hull Form Design by Response Surface Methodology. In: 24th Symposium on Naval Hydrodynamics, Fukuoka, Japan (2002)

    Google Scholar 

  18. Newman III, J.C., Pankajakshan, R., Whitfield, D.L., Taylor, L.K.: Computational Design Optimization Using RANS. In: 24th Symposium on Naval Hydrodynamics, Fukuoka, Japan (2002)

    Google Scholar 

  19. Miettinen, K.M.: Nonlinear multiobjective optimization. Kluwer Academic Publisher, Dordrecht (1999)

    MATH  Google Scholar 

  20. Myers, R.H., Montgomery, D.C.: Response Surface Methodology. Wiley, USA (1997)

    Google Scholar 

  21. Pareto, V.: Manuale di economia politica, Società editrice libraria (1906), Milano, Italy. Also in Manual of Political Economy. The MacMillan Press Ltd., Basingstoke (1971)

    Google Scholar 

  22. Peri, D., Rossetti, M., Campana, E.F.: Design optimization of ship hulls via CFD techniques. Journal of Ship Research 45(2), 140–149 (2001)

    Google Scholar 

  23. Peri, D., Campana, E.F., Di Mascio, A.: Development of CFD-based design optimization architecture. In: 1st MIT conference on fluid and solid mechanics, Cambridge, MA USA (2001)

    Google Scholar 

  24. Peri, D., Campana, E.F.: High fidelity models in the Multi-disciplinary Optimization of a frigate ship. In: 2nd MIT conference on fluid and solid mechanics, Cambridge, MA USA (2003)

    Google Scholar 

  25. Peri, D., Campana, E.F.: Multidisciplinary Design Optimization of a Naval Surface Combatant. Journal of Ship Research 47(1), 1–12 (2003)

    Google Scholar 

  26. Smith, M.: Neural networks for statistical modeling. Von Nostrand Reinhold, New York (1993)

    MATH  Google Scholar 

  27. Sobieszczanski-Sobieski, J., Haftka, R.T.: Multidisciplinary aerospace design optimisation: survey of recent developments. Struct. Optim. 14, 1–23 (1997)

    Article  Google Scholar 

  28. Solomatine, D.P.: Two strategies of adaptive cluster covering with descent and their comparison to other algorithms. Journal of Global Optimization 14(1), 55–78 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  29. Statnikov, R.B., Matusov, J.B.: Multicriteria optimization and engineering. Chapman & Hall, USA (1995)

    Google Scholar 

  30. Tahara, Y., Patterson, E., Stern, F., Himeno, Y.: Flow- and wave-field optimization of surface combatants using CFD-based optimization methods. In: 23rd ONR Symposium on Naval Hydrodynamics, Val de Reuil, France (2000)

    Google Scholar 

  31. Thomas, J.P., Hall, K.C., Dowell, E.H.: A disrete adjoint approach for modeling unsteady aerodynamic design sensitivities. In: 41-th Aerospace Science Meeting and Exibit, Reno, Nevada, USA (2003)

    Google Scholar 

  32. Törn, A.A., Žilinskas, A.: Global optimization. Springer, Berlin (1989)

    MATH  Google Scholar 

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Authors and Affiliations

  1. INSEAN, Via di Vallerano, 139, 00128, Roma, Italy

    Daniele Peri & Emilio F. Campana

Authors
  1. Daniele Peri
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  2. Emilio F. Campana
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Editor information

Editors and Affiliations

  1. LINA, FRE CNRS 2729 – University of Nantes, 2, rue de la Houssinière, BP 92208, F-44322, Nantes cedex 3, France

    Christophe Jermann

  2. Institute for Mathematics, University Wien, Nordbergstr. 15, A-1090, Wien, Austria

    Arnold Neumaier

  3. Artificial Intelligence Laboratory, Swiss Federal Institute of Technology, Route J.-D. Colladon, Bat. INR, Office 235, CH-1015, Lausanne, Switzerland

    Djamila Sam

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© 2005 Springer-Verlag Berlin Heidelberg

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Peri, D., Campana, E.F. (2005). High-Fidelity Models in Global Optimization. In: Jermann, C., Neumaier, A., Sam, D. (eds) Global Optimization and Constraint Satisfaction. COCOS 2003. Lecture Notes in Computer Science, vol 3478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425076_9

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  • DOI: https://doi.org/10.1007/11425076_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26003-5

  • Online ISBN: 978-3-540-32041-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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