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Evaluation of the different genetic algorithm parameters and operators for the finite element model updating problem

  • Received : 2010.10.05
  • Accepted : 2012.12.02
  • Published : 2013.06.01

Abstract

There is a wide variety of existing Genetic Algorithms (GA) operators and parameters in the literature. However, there is no unique technique that shows the best performance for different classes of optimization problems. Hence, the evaluation of these operators and parameters, which influence the effectiveness of the search process, must be carried out on a problem basis. This paper presents a comparison for the influence of GA operators and parameters on the performance of the damage identification problem using the finite element model updating method (FEMU). The damage is defined as reduction in bending rigidity of the finite elements of a reinforced concrete beam. A certain damage scenario is adopted and identified using different GA operators by minimizing the differences between experimental and analytical modal parameters. In this study, different selection, crossover and mutation operators are compared with each other based on the reliability, accuracy and efficiency criteria. The exploration and exploitation capabilities of different operators are evaluated. Also a comparison is carried out for the parallel and sequential GAs with different population sizes and the effect of the multiple use of some crossover operators is investigated. The results show that the roulettewheel selection technique together with real valued encoding gives the best results. It is also apparent that the Non-uniform Mutation as well as Parent Centric Normal Crossover can be confidently used in the damage identification problem. Nevertheless the parallel GAs increases both computation speed and the efficiency of the method.

Keywords

References

  1. Bakir, P.G., Reynders, E. and De Roeck, G. (2008), "An improved finite element model updating method by the global optimization technique Coupled Local Minimizers", Comput. Struct., 86(11-12), 1339-1352. https://doi.org/10.1016/j.compstruc.2007.08.009
  2. Bakir, P.G., Reynders, E. and De Roeck, G. (2007), "Sensitivity-based finite element model updating using constrained optimization with a trust region algorithm", J. Sound Vib., 305(1-2), 211-225. https://doi.org/10.1016/j.jsv.2007.03.044
  3. Bakir, P.G. (2011), "The combined deterministic stochastic subspace based system identification in buildings", Struct. Eng. Mech., 38(3), 315-332. https://doi.org/10.12989/sem.2011.38.3.315
  4. Bakir, P.G. (2012), "Instrumentation and system identification of a typical school building in Istanbul", Struct. Eng. Mech., 43(2), 179-197. https://doi.org/10.12989/sem.2012.43.2.179
  5. Bakir, P.G., Alkan, S. and Eksioglu, E.M. (2011), "A comparative study on the subspace based system identification techniques applied on civil engineering structures", Smart Struct. Syst., 7(2), 153-167. https://doi.org/10.12989/sss.2011.7.2.153
  6. Ballester, P.J. and Carter, J.N. (2004), An effective real-parameter genetic algorithm with parent centric normal crossover for multimodal optimisation, Genetic and Evolutionary Computation - GECCO, 901-913.
  7. Boyer, D.O. and Martinez, C.H. and Pedrajas, N.G. (2008), "Robust confidence intervals applied to crossover operator for real-coded genetic algorithms", Soft Comput., 12(8), 809-833. https://doi.org/10.1007/s00500-007-0237-0
  8. Chou, J.H. and Ghaboussi, J. (2001), "Genetic algorithm in structural damage detection", Comput. Struct., 79(14), 1335-1353. https://doi.org/10.1016/S0045-7949(01)00027-X
  9. Cruz, P.J.S and Salgado, R. (2009), "Performance of vibration-based damage detection methods in bridges", Comput.-Aided Civil Infrastruct. Eng., 24(1), 62-79. https://doi.org/10.1111/j.1467-8667.2008.00546.x
  10. Deep, K. and Thakur, M. (2007), "A new mutation operator for real coded genetic algorithms", Appl. Math. Comput., 193(1), 211-230. https://doi.org/10.1016/j.amc.2007.03.046
  11. Demsar, J. (2006), "Statistical comparisons of classifiers over multiple data sets", J. Mach. Learn. Res., 7(1), 71-30.
  12. Dep, K. and Agraval, R.B. (1995), "Simulated binary crossover for continuous search space", Complex Syst., 9, 115-148.
  13. Erdogan, Y.S. and Bakir, P.G. (2013), "Inverse propagation of uncertainties in finite element model updating through use of fuzzy numbers", Eng. Appl. Artif. Intel., 26(1), 357-367. https://doi.org/10.1016/j.engappai.2012.10.003
  14. Eshelman, L.J. and Schaffer, J.D. (1993), Real-coded genetic algorithms and interval schemata, D.L. Whitley (Ed.), Foundation of Genetic Algorithms II, Morgan Kaufmann, San Mateo, CA, 187-202.
  15. Friswell, M.I. and Mottershead, J.E. (1995), Finite element model updating in structural dynamics, Kluwer Academic Publishers, Dordrecht, The Netherlands.
  16. Garcian, S. and Herrera, F. (2008), "An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons", J. Mach. Learn. Res., 9(12), 2677-2694.
  17. Goldberg, D.E. (1989), Genetic algorithms in search, optimization, and machine learning, massachusetts, Addison-Wesley.
  18. Hao, H. and Xia, Y. (2002), "Vibration-based damage detection of structures by genetic algorithms", J. Comput. Civil Eng.-ASCE, 16(3), 222-229. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:3(222)
  19. Holland, J.H. (1975), Adaptation in natural and artificial systems, Ann Arbor: The University of Michigan press.
  20. Herrera, F., Lozana, M. and Sanchez, A.M. (2003), "A taxonomy for the crossover operator for real-coded genetic algorithms: an experimental study", Int. J. Intell. Syst., 18(3), 309-333. https://doi.org/10.1002/int.10091
  21. Higuchi, T., Tsitsui, S. and Yamamura, M. (2000), "Theoretical analysis of simplex crossover for real-coded genetic algorithms", Lecture Notes In Computer Science, 1917, Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, 365-374.
  22. Humar, J., Bagchi, A. and Xu, H. (2006), "Performance of vibration-based techniques for the identification of structural damage", Struct. Health Monit., 5(3), 215-227. https://doi.org/10.1177/1475921706067738
  23. Lee, K.Y. and El-Sharkawi, M.A. (2008), Modern heuristic techniques, John Wiley & Sons, Inc. Hoboken, New Jersey.
  24. Levin, L.I. and Lieven, N.A.J. (1998), "Dynamic finite element model updating using simulated annealing and genetic algorithms", Mech. Syst. Signal Pr., 12(1), 91-120. https://doi.org/10.1006/mssp.1996.0136
  25. Li, X.Y. and Law, S.S. (2010), "Consistent regularization for damage detection with noise and model errors", AIAA J., 48(4), 777-787. https://doi.org/10.2514/1.43322
  26. Lim, D., Ong, Y.S., Jin, Y., Sendhoff, B. and Lee, B.S. (2007), "Efficient hierarchical parallel genetic algorithms using grid computing", Future Gener. Comp. Sy., 23(4), 658-670. https://doi.org/10.1016/j.future.2006.10.008
  27. Makinen, R.A.E., Periaux, J. and Toivanen, J. (1999), "Multidisciplinary shape optimization in aerodynamics and electromagnetics using genetic algorithms", Int. J. Numer. Meth. Fl., 30,149-159. https://doi.org/10.1002/(SICI)1097-0363(19990530)30:2<149::AID-FLD829>3.0.CO;2-B
  28. Meruane, V and Heylen, W. (2010), "Damage detection with parallel genetic algorithms and operational modes", Struct. Health Monit., 9(6), 481-496. https://doi.org/10.1177/1475921710365400
  29. Michalewicz, Z. (1996), Genetic algorithms + data structure =evolution programs, Springer, New York.
  30. Ono, I. and Kobayashi, S. (1997), "A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover", Proceedings of 7th ICGA, 246-253.
  31. Perera, R. and Torres, R. (2005), "Structural damage detection via modal data with genetic algorithms", J. Struct. Eng., 132(9), 1491-1501.
  32. Rao, M.A., Srinivas, J. and Murthy, B.S.N. (2004), "Damage detection in vibrating bodies using genetic algorithm", Comput. Struct., 82(11-12), 963-968. https://doi.org/10.1016/j.compstruc.2004.01.005
  33. Teughels, A., Maeck, J. and De Roeck, G. (2002), "Damage assesment by FE model updating using damage functions", Comput. Struct., 80(25), 1869-1879. https://doi.org/10.1016/S0045-7949(02)00217-1
  34. Tsutsui, S., Yamamura, M. and Higuchi, T. (1999), "Multi-parent recombination with simplex crossover in real coded genetic algorithms", Proceedings of the Genetic and Evolutionary Computation Conference (GECC0-1999), 657-664, Morgan Kaufmann, San Francisco.
  35. Wright, A. (1991), Genetic algorithms for real parameter optimization, G.J.E. Rawlins (Ed.), Foundations of Genetic Algorithms, 205-220.
  36. Voight, H.M., Muhlenbein, H. and Cvetkovic, D. (1995), "Fuzzy recombination for the breeder genetic algorithm", Proceedings of the 6th Int Conf Genetic Algorithms, San Mateo, CA, Morgan Kaufmann, 104-111.

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