Search-Based Model Optimization Using Model Transformations

  • Joachim Denil
  • Maris Jukss
  • Clark Verbrugge
  • Hans Vangheluwe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8769)


Design-Space Exploration (DSE) and optimization look for a suitable and optimal candidate solution to a problem, with respect to a set of quality criteria, by searching through a space of possible solution designs. Search-Based Optimization (SBO) is a well-known technique for design-space exploration and optimization. Model-Driven Engineering (MDE) offers many benefits for creating a general approach to SBO, through a suitable problem representation. In MDE, model transformation is the preferred technique to manipulate models. The challenge thus lies in adapting model transformations to perform SBO tasks. In this paper, we demonstrate that multiple SBO techniques are easily incorporated into MDE. Through a non-trivial example of electrical circuit generation, we show how this approach can be applied, how it enables simple switching between different SBO approaches, and integrates domain knowledge, all within the modeling paradigm.


Simulated Annealing Model Transformation Candidate Solution Transformation Rule Hill Climbing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Burton, F.R., Paige, R.F., Rose, L.M., Kolovos, D.S., Poulding, S., Smith, S.: Solving acquisition problems using model-driven engineering. In: Vallecillo, A., Tolvanen, J.-P., Kindler, E., Störrle, H., Kolovos, D. (eds.) ECMFA 2012. LNCS, vol. 7349, pp. 428–443. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Burton, F.R., Poulding, S.M.: Complementing metaheuristic search with higher abstraction techniques. In: Paige et al. [19], pp. 45–48Google Scholar
  3. 3.
    Cook, S.A.: The complexity of theorem-proving procedures. In: Proc. Third Annual ACM Symp. on Theory of Computing (STOC 1971), pp. 151–158. ACM, USA (1971)CrossRefGoogle Scholar
  4. 4.
    Denil, J., Cicchetti, A., Biehl, M., De Meulenaere, P., Eramo, R., Demeyer, S., Vangheluwe, H.: Automatic Deployment Space Exploration Using Refinement Transformations. Electronic Communications of the EASST Recent Advances in Multi-paradigm Modeling 50 (2011)Google Scholar
  5. 5.
    Denil, J., Han, G., Persson, M., De Meulenaere, P., Zeng, H., Liu, X., Vangheluwe, H.: Model-Driven Engineering Approaches to Design Space Exploration. Tech. rep., McGill University, SOCS-TR-2013.1 (2013)Google Scholar
  6. 6.
    Denil, J., Jukss, M., Verbrugge, C., Vangheluwe, H.: Search-based model optimization using model transformation. Tech. Rep. SOCS-TR-2014.2, School of Computer Science, McGill University (January 2014)Google Scholar
  7. 7.
    Drago, M.L., Ghezzi, C., Mirandola, R.: QVTR2: A rational and performance-aware extension to the relations language. In: Dingel, J., Solberg, A. (eds.) MODELS 2010. LNCS, vol. 6627, pp. 328–328. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Hegedus, A., Horvath, A., Rath, I., Varro, D.: A model-driven framework for guided design space exploration. In: Proc. ASE 2011, pp. 173–182. IEEE CS, USA (2011)Google Scholar
  9. 9.
    Jackson, E.K., Kang, E., Dahlweid, M., Seifert, D., Santen, T.: Components, platforms and possibilities: Towards generic automation for mda. In: Proc. EMSOFT 2010, pp. 39–48. ACM, USA (2010)Google Scholar
  10. 10.
    Jukss, M., Verbrugge, C., Elaasar, M., Vangheluwe, H.: Scope in model transformations. Tech. Rep. SOCS-TR-2013.4, School of Computer Science, McGill University (January 2013)Google Scholar
  11. 11.
    Kessentini, M., Wimmer, M., Sahraoui, H., Boukadoum, M.: Generating transformation rules from examples for behavioral models. In: Proc. Second International Workshop on Behaviour Modelling Foundation and Applications, BM-FA 2010, pp. 1–7. ACM Press, New York (2010)CrossRefGoogle Scholar
  12. 12.
    Kessentini, M., Langer, P., Wimmer, M.: Searching models, modeling search: On the synergies of SBSE and MDE. In: Paige et al. [19], pp. 51–54Google Scholar
  13. 13.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Land, A., Doig, A.: An Automated Method of Solving Discrete Programming Problems. Econometrica 28(3), 497–520 (1960)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Lúcio, L., Mustafiz, S., Denil, J., Vangheluwe, H., Jukss, M.: FTG+PM: An integrated framework for investigating model transformation chains. In: Khendek, F., Toeroe, M., Gherbi, A., Reed, R. (eds.) SDL 2013. LNCS, vol. 7916, pp. 182–202. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  16. 16.
    Mustafiz, S., Denil, J., Lúcio, L., Vangheluwe, H.: The FTG+PM framework for multi-paradigm modelling: An automotive case study. In: Proc. MPM 2012, pp. 13–18. ACM, USA (2012)Google Scholar
  17. 17.
    Nagel, L., Pederson, D.: SPICE (Simulation Program with Integrated Circuit Emphasis). Tech. Rep. UCB/ERL M382, EECS Department, University of California, Berkeley (April 1973)Google Scholar
  18. 18.
    Neema, S., Sztipanovits, J., Karsai, G., Butts, K.: Constraint-based design-space exploration and model synthesis. In: Alur, R., Lee, I. (eds.) EMSOFT 2003. LNCS, vol. 2855, pp. 290–305. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  19. 19.
    Paige, R.F., Harman, M., Williams, J.R. (eds.): CMSBSE@ICSE 2013. IEEE CS (2013)Google Scholar
  20. 20.
    Saxena, T., Karsai, G.: Mde-based approach for generalizing design space exploration. In: Petriu, D.C., Rouquette, N., Haugen, Ø. (eds.) MODELS 2010, Part I. LNCS, vol. 6394, pp. 46–60. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Schätz, B., Hölzl, F., Lundkvist, T.: Design-Space Exploration through Constraint-Based Model-Transformation. In: 2010 17th IEEE International Conference and Workshops on Engineering of Computer-Based Systems, pp. 173–182. IEEE (2010)Google Scholar
  22. 22.
    Schmidt, D.C.: Guest Editor’s Introduction: Model-Driven Engineering. IEEE Computer 39(2), 25–31 (2006)CrossRefGoogle Scholar
  23. 23.
    Sen, S., Baudry, B., Vangheluwe, H.: Towards domain-specific model editors with automatic model completion. Simulation 86(2), 109–126 (2010)CrossRefGoogle Scholar
  24. 24.
    Sendall, S., Kozaczynski, W.: Model transformation: the heart and soul of model-driven software development. IEEE Software 20(5), 42–45 (2003)CrossRefGoogle Scholar
  25. 25.
    Syriani, E., Vangheluwe, H., LaShomb, B.: T-Core: a framework for custom-built model transformation engines. Software & Systems Modeling, 1–29 (2013)Google Scholar
  26. 26.
    Williams, J.R., Poulding, S., Rose, L.M., Paige, R.F., Polack, F.A.C.: Identifying desirable game character behaviours through the application of evolutionary algorithms to model-driven engineering metamodels. In: Cohen, M.B., Ó Cinnéide, M. (eds.) SSBSE 2011. LNCS, vol. 6956, pp. 112–126. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  27. 27.
    Zeng, H., Natale, M.D.: Improving real-time feasibility analysis for use in linear optimization methods. In: 22nd Euromicro Conference on Real-Time Systems (ECRTS), pp. 279–290. IEEE CS (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Joachim Denil
    • 1
    • 2
  • Maris Jukss
    • 2
  • Clark Verbrugge
    • 2
  • Hans Vangheluwe
    • 1
    • 2
  1. 1.University of AntwerpBelgium
  2. 2.McGill UniversityCanada

Personalised recommendations