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Asynchronous evolutionary shape optimization based on high-quality surrogates: application to an air-conditioning duct

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Abstract

Multi-processor HPC tools have become commonplace in industry and research today. Evolutionary algorithms may be elegantly parallelized by broadcasting a whole population of designs to an array of processors in a computing cluster or grid. However, issues arise due to synchronization barriers: subsequent iterations have to wait for the successful execution of all jobs of the previous generation. When other users load a cluster or a grid, individual tasks may be delayed and some of them may never complete, slowing down and eventually blocking the optimization process. In this paper, we extend the recent “Futures” concept permitting the algorithm to circumvent such situations. The idea is to set the default values to the cost function values calculated using a high-quality surrogate model, progressively improving when “exact” numerical results are received. While waiting for the exact result, the algorithm continues using the approximation and when the data finally arrives, the surrogate model is updated. At convergence, the final result is not only an optimized set of designs, but also a surrogate model that is precise within the neighborhood of the optimal solution. We illustrate this approach with the cluster optimization of an A/C duct of a passenger car, using a refined CFD legacy software model along with an adaptive meta-model based on Proper Orthogonal Decomposition (POD) and diffuse approximation.

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References

  1. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  2. Vose MD (1999) The simple genetic algorithm: foundations and theory. MIT Press, Cambridge

    MATH  Google Scholar 

  3. Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91:992–1007

    Article  Google Scholar 

  4. Deb K (2001) Multi-objective optimization using genetic algorithms. Wiley, Chichester

    Google Scholar 

  5. Willcox K, Peraire J (2002) Balanced model reduction via the proper orthogonal decomposition. AIAA Journal 40(11):2323–2330

    Article  Google Scholar 

  6. Gorissen D, Couckuyt I, Laermans E, Dhaene T (1985) Multiobjective global surrogate modeling, dealing with the 5-percent problem. Eng Comput 26(1):81–98

    Article  Google Scholar 

  7. Lim D, Jin YC, Ong YS, Sendhoff B (2010) Generalizing surrogate-assisted evolutionary computation. IEEE Trans Evol Comput 14(3):329–355

    Article  Google Scholar 

  8. Quiepo NV, Verde A, Pintos S, Haftka RT (2009) Assessing the value of another cycle in Gaussian process surrogate-based optimization. Int J Struc Multidisc Optim 39(5):459–475

    Article  Google Scholar 

  9. Viana FAC, Haftka RT, Steffen V (2009) Multiple surrogates: how cross-validation errors can help us to obtain the best predictor. Int J Struc Multidisc Optim 39(4):439–457

    Article  Google Scholar 

  10. Knowles J (2006) ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multi objective optimization problems. IEEE Trans Evol Comput 10(1):50–66

    Article  Google Scholar 

  11. Jones D, Schonlau M, Welch W (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455–492

    Article  MathSciNet  MATH  Google Scholar 

  12. Berkooz G, Holmes P, Lumley JL (1993) The proper orthogonal decomposition in the analysis of turbulent flows. Annu Rev Fluid Mech 25:539–575

    Article  MathSciNet  Google Scholar 

  13. Filomeno Coelho R, Breitkopf P, Knopf-Lenoir C (2008) Model reduction for multidisciplinary optimization—application to a 2d wing. Int J Struc Multidisc Optim 37(1):29–48

    Article  Google Scholar 

  14. Filomeno Coelho R, Breitkopf P, Knopf-Lenoir C (2009) Bi-level model reduction for coupled problems. Int J Struc Multidisc Optim 39(4):401–418

    Article  MathSciNet  Google Scholar 

  15. Xiao M, Breitkopf P, Coelho RF, Knopf-Lenoir C, Sidorkiewicsz M, Villon P (2009) Model reduction by CPOD and Kriging. Int J Struc Multidisc Optim 41(4):555–574

    Article  Google Scholar 

  16. Bethke AD (1976) Comparison of genetic algorithms and gradient-based optimizers on parallel processors: efficiency of use of processing capacity, Tech rep no 197. University of Michigan, Ann Arbor

    Google Scholar 

  17. Greffensette JJ (1981) Parallel adaptive algorithms for function optimization: parallel subcomponent interaction in a multilocus model, Tech Rep No CS-81-19. Vanderbilt University, Nashville

    Google Scholar 

  18. Cantu-Paz E (1997) A survey of parallel genetic algorithms IllGAL report 97003. The University of Illinois, Chicago

    Google Scholar 

  19. Tsutsui S (2010) Parallelization of an evolutionary algorithm on a platform with multi-core processors. Artificial evolution, vol 5975. Lecture notes in computer science. Springer, Heidelberg, pp 61–73

    Google Scholar 

  20. Wu H, Xu CL, Zou XF (2009) An efficient asynchronous parallel evolutionary algorithm based on message passing model for solving complex nonlinear constrained optimization. In: proceedings of the 8th international symposium on operations research and its applications, ZhangJiaJie, China

  21. Regis RG, Shoemaker CA (2009) Parallel stochastic global optimization using radial basis functions. INFORMS J Comput 21(3):411–426

    Article  MathSciNet  MATH  Google Scholar 

  22. Asouti VG, Kampolis IC, Giannakoglou KC (2009) A grid-enabled asynchronous meta model-assisted evolutionary algorithm for aerodynamic optimization. Genet Program Evolvable Mach 10(4):373–389

    Article  Google Scholar 

  23. LeRiche R, Collette Y, Hansen N, Pujol G, Salazar D (2010) On object-oriented programming of optimizers: examples in Scilab. In: P. Breitkopf, R. Filomeno Coehlo (eds) Multidisciplinary design optimization in computational mechanics (chapter 14) Wiley/ISTE, Ney York, June 2010, pp 499–538

  24. Caromel D, Henrio L (2004) A theory of distributed objects. Springer, Berlin

    Google Scholar 

  25. http://omd2.scilab.org/ (2009) OMD2-project home-page, Accessed Feb 22 2011

  26. http://www.openfoam.com OpenFoam: the open-source CFD toolbox, Accessed Aug 17 2010

  27. Breitkopf P (1998) An algorithm for construction of iso-valued surfaces for finite elements. Eng Comput 14(2):146–149

    Article  Google Scholar 

  28. Rypl D, Krysl P (1997) Triangulation of 3D surfaces. Eng Comput 13(2):87–98

    Article  Google Scholar 

  29. Breitkopf P, Rassineux A, Touzot G, Villon P (2000) Explicit form and efficient computation of MLS shape functions and their derivatives. Int J Numer Meth Eng 48:451–456

    Article  MATH  Google Scholar 

  30. Ryckelynck D (2005) A priori hyper eduction method: an adaptive approach. J Comput Phys 202(1):346–366

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work has been supported by the French National Research Agency (ANR), through the COSINUS program (project OMD2 no. ANR-08-COSI-007). The authors acknowledge the Projet Pluri-Formations PILCAM2 at the Université de Technologie de Compiègne for providing HPC resources that have contributed to the research results reported within this paper (URL: http://pilcam2.wikispaces.com.) as well as Maryan Sidorkiewicsz, Direction de la Recherche, Renault, France and Mr. V. Picheny, Ecole des Mines, France for contributing the CFD model used in this work.

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  1. Laboratoire Roberval, UTC-CNRS (UMR 7337), Labex MS2T, Université de Technologie de Compiègne, Compiègne, France

    Balaji Raghavan & Piotr Breitkopf

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  1. Balaji Raghavan
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  2. Piotr Breitkopf
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Correspondence to Piotr Breitkopf.

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Raghavan, B., Breitkopf, P. Asynchronous evolutionary shape optimization based on high-quality surrogates: application to an air-conditioning duct. Engineering with Computers 29, 467–476 (2013). https://doi.org/10.1007/s00366-012-0263-0

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