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On improved evolutionary algorithms application to the physically based approximation of experimental data

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Języki publikacji
EN
Abstrakty
EN
In this paper an evolutionary algorithms (EA) application to the physically based approximation (PBA) of experimental and/or numerical data is considered. Such an approximation may simultaneously use the whole experimental, theoretical and heuristic knowledge about the analyzed problems. The PBA may be also applied for smoothing discrete data obtained from any rough numerical solution of the boundary value problem, and for solving inverse problems as well, like reconstruction of residual stresses based on experimental data. The PBA presents a very general approach formulated as a large non-linear constrained optimization problem. Its solution is usually complex and troublesome, especially in the case of non-convex problems. Here, considered is a solution approach of such problems based on the EA. However, the standard EA are rather slow methods, especially in the final stage of optimization process. In order to increase their solution efficiency, several acceleration techniques were introduced. Various benchmark problems were analyzed using the improved EA. The intended application of this research is reconstruction of residual stresses in railroads rails and vehicle wheels based on neutronography measurements.
Rocznik
Strony
27--38
Opis fizyczny
Bibliogr. 20 poz., wykr.
Twórcy
autor
  • Institute for Computational Civil Engineering, Cracow University of Technology, Poland
autor
  • Institute for Computational Civil Engineering, Cracow University of Technology, Poland
Bibliografia
  • [1] M. Ainsworth, J.T. Oden. A-posteriori error estimation in finite element analysis. Computer Methods in Applied Mechanics and Engineering, 142: 1–88, 1997.
  • [2] T. Burczynski, P. Orantek. Evolutionary and hybrid algorithms. In: R.R. Gajewski, ed., Neural Networks, Genetic Algorithms, Fuzzy Sets [in Polish], 99–117. BEL, Rzeszow, 1999.
  • [3] A.P. Engelbrecht. Computational intelligence: an introduction. Wiley, Chichester, 2007.
  • [4] M. Głowacki, J. Orkisz. Advances in development of dedicated evolutionary algorithms for large non-linear constrained optimization problems. IPPT Reports on Fundamental Technological Research, 47(4): 25–29, 2013.
  • [5] C. Grosan, A. Abraham, H. Ishibuchi, eds. Hybrid evolutionary algorithms. Studies in Computational Intelligence, 75, Springer, 2007.
  • [6] R. Hill. The Mathematical theory of plasticity. Oxford University Press, New York, 2004.
  • [7] K. Holak, P. Kohut, A. Martowicz, T. Uhl. An uncertainty analysis for developed measurement vision system aided by numerical simulations. Mechanics and Control, 30(2): 65–72, 2011.
  • [8] W. Karmowski, J. Orkisz. Physically based method of enhancement of experimental data – concepts, formulation and application to identification of residual stresses. Proc. of IUTAM Symp. On Inverse Problems in Engng Mech., Tokyo, 1992. In: M. Tanaka, H.D. Bui, eds., On Inverse Problems in Engineering Mechanics, 61–70, Springer-Verlag, 1993.
  • [9] W. Kus, T. Burczynski. Parallel bioinspired algorithms in optimization of structures. Lecture Notes in Computational Sciences, 4967/2008: 1285–1292, Springer, 2008.
  • [10] Z. Michalewicz. Genetic algorithms + data structures = evolution programs. Springer-Verlag, Berlin Heidelberg, 1996.
  • [11] N. Nedjah, E. Alba, L. Mourelle, eds. Parallel evolutionary computations. Studies in Computational Intelligence, 22, Springer, 2006.
  • [12] J. Orkisz. Prediction of actual residual stresses by constrained minimization of energy. In: O. Orringer, J. Orkisz, Z. Swiderski, eds., Residual Stress in Rails, Vol. 2, 101–124. Kluwer Academic Publisher, 1992.
  • [13] J. Orkisz et al. Development of advanced methods for theoretical prediction of shakedown stress states and physically based enhancement of experimental data, US DOT report, DTFR53-03-G-00012, Cracow, 2004.
  • [14] J. Orkisz, W. Cecot, Prediction of actual residual stresses resulting from cyclic loading in kinematic hardening material. In: E. Onate, D.R.J. Owen, eds., Proc. of the 5th Int. Conf. on Computational Plasticity, 1039–1042, CIMNE, Barcelona, 1997.
  • [15] J. Orkisz, M. Głowacki. On acceleration of evolutionary algorithms taking advantage from aposteriori error analysis. Computing and Informatics, 33(1): 154–174, 2014.
  • [16] J. Orkisz, M. Głowacki. On certain improvements for evolutionary algorithms applied to residual stresses analysis. In: T. Łodygowski et al., eds., CMM 2013 Short Papers, MS05-7-8, Poznań University of Technology, Poznań, 2013.
  • [17] J. Orkisz, M. Głowacki. On stress reconstruction using experimental data the PBA and accelerated EA. In: Z. Waszczyszyn, L. Ziemiański, eds., ECCOMAS IPM 2013. Conference Proceedings, 43–44, Rzeszow University of Technology, Rzeszów, 2013.
  • [18] J. Orkisz, M. Głowacki, A. Kleszcz. On efficient evolutionary algorithms solution approach to physically based approximation. In: T. Burczyński, J. Periaux, eds., EUROGEN 2009 Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems. Book of Abstracts, 85–86, Kraków/Gliwice, 2009.
  • [19] J. Orkisz, T. Liszka. The finite difference method at arbitrary irregular grids and its applications in applied mechanics. Computers and Structures, 11: 83–95, 1980.
  • [20] K. Salkauskas, P. Lancaster. Curve and surface fitting. Academic Press, 1990.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6a49e027-348a-46a9-b283-403faafdf859
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