Skip to main content
SpringerLink
Log in

Data driven surrogate-based optimization in the problem solving environment WBCSim

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due to the complexity of the underlying simulation codes. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate-based optimization algorithm that uses a trust region-based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from two packages—SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques—full factorial (FF), Latin hypercube sampling, and central composite design—are used to train the surrogates. The results are compared with the optimization results obtained by directly coupling an optimizer with the simulation code. The biggest concern in using the SAO framework based on statistical sampling is the generation of the required database. As the number of design variables grows, the computational cost of generating the required database grows rapidly. A data driven approach is proposed to tackle this situation, where the trick is to run the expensive simulation if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the proposed methodology dramatically reduces the total number of calls to the expensive simulation runs during the optimization process.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Allen NA, Shaffer CA, Vass MT, Ramakrishnan N, Watson LT (2003) Improving the development process for eukaryotic cell cycle models with a modeling support environment. Simulation 79:674–688

    Article  Google Scholar 

  2. Ames AL, Nadeau DR, Moreland JL (1996) VRML 2.0 Sourcebook, 2nd edn. Wiley, New York, pp 241–295

    Google Scholar 

  3. Burnett T, Chaput C, Arrighi H, Norris J, Suson DJ (2000) Simulating the Glast satellite with Gismo. IEEE Comput Sci Eng 2:9–18

    Google Scholar 

  4. Bramley R, Gannon D, Stuckey T, Villacis J, Akman E, Balasubramanian J, Breg F, Diwan S, Govindaraju M (1998) The linear system analyzer, technical Report TR-511. Department of Computer Science, Indiana University, Bloomington, IN

  5. Boisvert RF, Rice JR (1985) Solving elliptic problems using ELLPACK. Springer, New York

    MATH  Google Scholar 

  6. Chen JX, Fu X (1999) Integrating physics-based computing and visualization: modeling dust behavior. IEEE Comput Sci Eng 1:12–16

    Google Scholar 

  7. Dymond R, Lohani V, Kibler D, Bosch D, Rubin EJ, Dietz R, Chanat J, Speir C, Shaffer CA, Ramakrishnan N, Watson LT (2003) From landscapes to waterscapes: a PSE for landuse change analysis. Eng Comput 19:9–25

    Article  Google Scholar 

  8. Eldred MS, Hart WE (1998) Design and implementation of multilevel parallel optimization on the intel teraflops. Multidiscip Anal Optim 98:44–54

    Google Scholar 

  9. Gallopoulos E, Houstis E, Rice JR (1994) Computer as thinker/doer: problem solving environments for computational science. IEEE Comput Sci Eng 1:11–23

    Article  Google Scholar 

  10. Goel A, Phanouriou C, Kamke FA, Ribbens CJ, Shaffer CA, Watson LT (1999) WBCSim: a prototype problem solving environment for wood-based composites simulations. Eng Comput 15:198–210

    Article  Google Scholar 

  11. Guisset P, Tzannetakis N (1997) Numerical methods for modeling and optimization of noise emission applications. In: Proceedings of the ASME international mechanical engineering congress and exposition, ASME FAIRFIELD, NJ, (USA), vol 24, pp 315–322

  12. Goel A, Baker CA, Shaffer CA, Grossman B, Mason WH, Watson LT, Haftka RT (2001) VizCraft: a problem solving environment for aircraft configuration design. IEEE Comput Sci Eng 3:56–66

    Google Scholar 

  13. Giunta AA, Richards MD, Cyr EC, Swiler LP, Brown SL, Eldred MS (2006) Surfpack version 1.0 user’s manual. Sandia National Laboratories, Albuquerque

    Google Scholar 

  14. Giunta AA, Swiler LP, Brown SL, Eldred MS, Richards MD, Cyr EC (2006) The Surfpack software library for surrogate modeling of sparse irregularly spaced multidimensional data. In: 11th AIAA/ISSMO multidisciplinary analysis and optimization conference, AIAA 1708-1736, Portsmouth, VA

  15. Houstis E, Gallopoulos E, Bramley R, Rice JR (1997) Problem solving environments for computational science. IEEE Comput Sci Eng 4:18–21

    Article  Google Scholar 

  16. Kamke FA, Wilson JB (1985) Computer simulation of a rotary dryer: retention time. Am Inst Chem Eng J 32:263–268

    Google Scholar 

  17. Kamke FA, Wilson JB (1985) Computer simulation of a rotary dryer: heat and mass transfer. Am Inst Chem Eng J 32:269–275

    Google Scholar 

  18. Myers RH, Montgomery DC (1995) Response surface methodology, process and product optimization using designed experiments. Wiley, New York

    MATH  Google Scholar 

  19. Mishra D, Shaffer CA, Ramakrishnan N, Watson LT, Bae KK, He J, Verstak A, Tranter WH (2007) S4W: a problem solving environment for wireless system design. Softw Pract Exp 37:1539–1558

    Article  Google Scholar 

  20. Pérez VM, Renaud JE, Watson LT (2001) Adaptive experimental design for construction of response surface approximation. AIAA 40:2495–2503

    Article  Google Scholar 

  21. Pérez VM, Renaud JE, Watson LT (2008) Reduced sampling for construction of quadratic response surface approximations using adaptive experimental design. Eng Comput 25:764–782

    Article  Google Scholar 

  22. Pérez VM, Renaud JE, Gano SE (2000) Constructing variable fidelity response surface approximations in the usable feasible region. In: Proceedings of the 8th AIAA/NASA/USAF multidisciplinary analysis & optimization symposium, AIAA 2000-4888, Long Beach, CA

  23. Pérez VM, Renaud JE (2000) Decoupling the design sampling region from the trust region in approximate optimization. In: Proceedings of the international mechanical engineering congress and exposition, American Society of Mechanical Engineers, vol 63, pp 205–214

    Google Scholar 

  24. Rodríguez JF, Perez VM, Padmanabhan D, Renaud JE (2001) Sequential approximate optimization using variable fidelity response surface approximations. Struct Multidiscip Optim 22:24–34

    Article  Google Scholar 

  25. Rodríguez JF, Renaud JE, Watson LT (1998) Trust region augmented Lagrangian methods for sequential response surface approximation and optimization. J Mech Design 120:58–66

    Article  Google Scholar 

  26. Resnik J, Kamke FA (1998) Modeling the cure of adhesive-wood bonds using high frequency energy. Final Report, U.S.-Slovene Joint Board on Scientific and Technological Cooperation, Project 95-AES10. University of Ljubljana, Ljubljana

  27. Rodríguez JF, Renaud JE, Watson LT (1998) Convergence of trust region augmented Lagrangian methods using variable fidelity approximation data. Struct Optim 15:141–156

    Article  Google Scholar 

  28. Renaud JE, Gabriele GA (1994) Approximation in nonhierarchic system optimization. AIAA J 32:198–205

    Article  MATH  Google Scholar 

  29. Shu J, Watson LT, Ramakrishnan N, Kamke FA, Zombori BG (2004) An experiment management component for the WBCSim problem solving environment. Adv Eng Softw 35:115–123

    Article  Google Scholar 

  30. Shu J, Watson LT, Ramakrishnan N, Kamke FA, North C (2008) Unification of problem solving environment implementation layers with XML. Adv Eng Softw 39:189–201

    Article  Google Scholar 

  31. Shu J, Watson LT, Zombori BG, Kamke FA (2006) WBCSim: an environment for modeling wood-based composites manufacture. Eng Comput 21:259–271

    Article  Google Scholar 

  32. Sioson A, Watkinson JI, Vasquez-Robinet C, Ellis M, Shukla M, Kumar D, Ramakrishnan N, Heath LS, Grene, R, Chevone BI, Kafadar K, Watson LT (2003) Expresso and chips: creating a next generation microarray experiment management system. In: Proceedings of the next generation software workshop, Los Alamitos, California, USA

  33. Skidmore R, Verstak A, Ramakrishnan N, Rappaport TS, Watson LT, He J, Varadarajan S, Shaffer CA, Chen J, Bae KK, Jiang J, Tranter WH (2004) Towards integrated PSEs for wireless communications: experiences with the S4W and SitePlanner projects. ACM SIGMOBILE Mobile Comput Commun Rev 8:20–34

    Article  Google Scholar 

  34. Tong SS, Powell D, Goel S (1992) Integration of artificial intelligence and numerical optimization techniques for the design of complex aerospace systems. In: Proceedings of the aerospace design conference, AIAA 92-1189, Irvine, CA

  35. Thacker WI, Zhang J, Watson LT, Birch JB, Iyer MA, Barry MW (2009) Algorithm XXX: SHEPPACK: modified Shepard algorithm for interpolation of scattered multivariate data. Technical Report TR 09-13, Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061

  36. Vanderplaats Research and Development, Inc. (1985) DOT users manual. Version 4.20, Colorado Springs, CO

  37. Vass M, Allen N, Shaffer CA, Ramakrishnan N, Watson LT, Tyson JJ (2004) The JigCell model builder and run manager. Bioinformatics 20:3680–3681

    Article  Google Scholar 

  38. Vass M, Shaffer CA, Ramakrishnan N, Watson LT, Tyson JJ (2006) The JigCell model builder: a spreadsheet interface for creating biochemical reaction network models. IEEE/ACM Trans Comput Biol Bioinforma 3:155–164

    Article  Google Scholar 

  39. Watson LT, Lohani VK, Kibler DF, Dymond RL, Ramakrishnan, N, Shaffer CA (2002) Integrated computing environments for watershed management. J Comput Civil Eng 16:259–268

    Article  Google Scholar 

  40. Wolfram S (1996) The mathematica book, 3rd edn. Wolfram Media/Cambridge University Press, Champaign

    Google Scholar 

  41. Wujek BA, Renaud JE (1998) New adaptive move-limit management strategy for approximate optimization, part 1. AIAA J 36:1911–1921

    Article  Google Scholar 

  42. Wujek BA, Renaud JE (1998) New adaptive move-limit management strategy for approximate optimization, part 2. AIAA J 36:1922–1937

    Article  Google Scholar 

  43. Zombori BG, Kamke FA, Watson LT (2001) Simulation of the mat formation process. Wood Fiber Sci 33:564–579

    Google Scholar 

  44. Zombori BG, Kamke FA, Watson LT (2003) Simulation of internal conditions during the hot-pressing process. Wood Fiber Sci 35:2–23

    Google Scholar 

  45. Zombori BG, Kamke FA, Watson LT (2004) Sensitivity analysis of internal mat environment during hot-pressing. Wood Fiber Sci 36:195–209

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by Department of Energy Grant DE-FG02-06ER25720, Air Force Research Laboratory Grant FA8650-09-2-3938, National Science Foundation Grant CCF-0726763, and Air Force Office of Scientific Research Grant FA9550-09-1-0153.

Author information

Authors and Affiliations

  1. Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061-0106, USA

    S. Deshpande, L. T. Watson, J. Shu & N. Ramakrishnan

  2. Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061-0106, USA

    L. T. Watson

  3. Department of Wood Science and Engineering, Oregon State University, Corvallis, OR, 97331, USA

    F. A. Kamke

Authors
  1. S. Deshpande
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. T. Watson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. A. Kamke
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. N. Ramakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Deshpande.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Deshpande, S., Watson, L.T., Shu, J. et al. Data driven surrogate-based optimization in the problem solving environment WBCSim. Engineering with Computers 27, 211–223 (2011). https://doi.org/10.1007/s00366-010-0192-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-010-0192-8

Keywords

Search

Navigation