Trajectory Optimization under Changing Conditions through Evolutionary Approach and Black-Box Models with Refining

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)

Abstract

This article provides an algorithm that is dedicated to repeated trajectory optimization with a fixed horizon and addresses processes that are difficult to describe by the established laws of physics. Typically, soft-computing methods are used in such cases, i.e. black-box modeling and evolutionary optimization. Both suffer from high dimensions that make the problems complex or even computationally infeasible. We propose a way how to start from very simple problems and - after the simple problems are covered sufficiently - proceed to more complex ones. We provide also a case study related to the dynamic optimization of the HVAC (heating, ventilation, and air conditioning) systems.

Keywords

Empirical function minimization black-box modeling simplification refining dynamic building control 

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References

  1. 1.
    Cleveland, W.: Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74(368), 829–836 (1979)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 312–317. IEEE (1996)Google Scholar
  3. 3.
    Henze, G., Kalz, D., Liu, S., Felsman, C.: Experimental analysis of model based predictive optimal control for active and passive building thermal storage inventory. HVAC&R Res. 11(1), 183–213 (2004)Google Scholar
  4. 4.
    Jolliffe, I.: Principal Component Analysis. Springer, New York (1986)CrossRefGoogle Scholar
  5. 5.
    Macek, K., Boštík, J., Kukal, J.: Reinforcement learning in global optimization heuristics. In: Mendel, 16th International Conference on Soft Computing, pp. 22–28 (2010)Google Scholar
  6. 6.
    Powell, W.B.: Approximate Dynamic Programming: Solving the Curses of Dimensionality. John Wiley & Sons, Hoboken (2007)CrossRefGoogle Scholar
  7. 7.
    Rojas, R.: Neural Networks - A Systematic Introduction. Springer, Berlin (1996)MATHGoogle Scholar
  8. 8.
    Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization. J. Global Optim. 11, 341–359 (1997)Google Scholar
  9. 9.
    Střelec, M., Macek, K., Abate, A.: Modeling and simulation of a microgrid as a stochastic hybrid system. In: Proceedings of the IEEE PES Innovative Smart Grid Technologies, ISGT 2012 (2012)Google Scholar
  10. 10.
    Tvrdík, J.: Adaptation in differential evolution: A numerical comparison. Appl. Soft Comput. 9(3), 1149–1155 (2009)CrossRefGoogle Scholar
  11. 11.
    Wasserman, L.: All of Nonparametric Statistics. Springer, New York (2006)MATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Karel Macek
    • 1
    • 2
  • Jiří Rojíček
    • 1
  • Vladimír Bičík
    • 1
  1. 1.Honeywell LaboratoriesPragueCzech Republic
  2. 2.Institute of Information Theory and AutomatizationAcademy of Scienced of the Czech RepublicPragueCzech Republic

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