Multi-objective Damage Identification Using Particle Swarm Optimization Techniques

  • Ricardo Perera
  • Sheng-En Fang
Part of the Studies in Computational Intelligence book series (SCI, volume 261)


The implementation of a technique that is able to detect the real state of a structure in near real time constitutes a key research field for guaranteeing the integrity of a structure and, therefore, for safeguarding human lives. This chapter presents particle swarm optimization-based strategies for multiobjective structural damage identification. Different variations of the conventional PSO based on evolutionary concepts are implemented for detecting the damage of a structure in a multiobjective framework.


Particle Swarm Optimization Pareto Front Multiobjective Optimization Particle Swarm Optimization Algorithm Damage Detection 
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.


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  1. 1.
    Ewins, D.J.: Modal testing: Theory and practice. Wiley, New York (1984)Google Scholar
  2. 2.
    Doebling, S.W., Farrar, C.R., Prime, M.B.: A summary review of vibration-based damage identification methods. Shock Vibration 30(2), 91–105 (1998)CrossRefGoogle Scholar
  3. 3.
    Yan, Y.J., Cheng, L., Wu, Z.Y., Yam, L.H.: Development in vibration-based structural damage detection technique. Mechanical Systems and Signal Processing 21, 2198–2211 (2007)CrossRefGoogle Scholar
  4. 4.
    Brownjohn, J.M.W., Xia, P.Q., Hao, H., Xia, Y.: Civil structure condition assessment by FE model updating methodology and case studies. Finite Elements in Analysis and Design 37, 761–775 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Perera, R., Torres, R.: Structural damage detection via modal data with genetic algorithms. Journal of Structural Engineering ASCE 132(9), 1491–1501 (2006)CrossRefGoogle Scholar
  6. 6.
    Friswell, M.J.: Damage identification using inverse methods. Philosophical Transactions of the Royal Society 365(1851), 393–410 (2007)CrossRefGoogle Scholar
  7. 7.
    Haralampidis, Y., Papadimitriou, C., Pavlidou, M.: Multiobjective framework for structural model identification. Earthquake Engineering and Structural Dynamics 34, 665–685 (2005)CrossRefGoogle Scholar
  8. 8.
    Perera, R., Ruiz, A., Manzano, C.: An evolutionary multiobjective framework for structural damage localization and quantification. Engineering Structures 29(10), 2540–2550 (2007)CrossRefGoogle Scholar
  9. 9.
    Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3, 1–16 (1995)CrossRefGoogle Scholar
  10. 10.
    Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary algorithms for solving multiobjective problems. Kluwer Academic Publishers, New York (2002)Google Scholar
  11. 11.
    Lagaros, N.D., Plevris, V., Papadrakakis, M.: Multi-objective design optimization using cascade evolutionary computations. Computer Methods for Applied Mechanics and Engineering 194, 3496–3515 (2005)zbMATHCrossRefGoogle Scholar
  12. 12.
    Coello, C.A.: Recent trends in evolutionary multiobjective optimization. In: Abraham, A., Jain, L.C., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization:Theoretical Advances and Applications. Springer, London (2005)Google Scholar
  13. 13.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, New Jersey, pp. 1942–1948 (1995)Google Scholar
  14. 14.
    Zhang, C., Shao, H., Li, Y.: Particle swarm optimization for evolving artificial neural network. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2487–2490 (2000)Google Scholar
  15. 15.
    Kennedy, J., Eberthart, R.C., Shi, Y.: Swarm Intelligence. Morgan Haufman Publishers, San Francisco (2002)Google Scholar
  16. 16.
    Eberthart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, pp. 81–86 (2001)Google Scholar
  17. 17.
    Abido, M.A.: Optimal design of power system stabilizers using particle swarm optimization. IEEE Transactions on Energy Conversion 17(3), 406–413 (2002)CrossRefGoogle Scholar
  18. 18.
    Agrafiotis, D.K., Cedeno, W.: selection for structure-activity correlation using binary particle swarms. Journal of Medicinal Chemistry 45(5), 1098–1107 (2002)CrossRefGoogle Scholar
  19. 19.
    Coello, C.A., Lechuga, M.S.: MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, pp. 1677–1681 (2002)Google Scholar
  20. 20.
    Hu, X., Eberhart, R.: Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, pp. 1677–1681 (2002)Google Scholar
  21. 21.
    Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optmization method in multiobjective problems. In: Proceedings of the 2002 ACM Symposium on Applied Computing, Madrid, pp. 603–607 (2002)Google Scholar
  22. 22.
    Hu, X., Shi, Y., Eberhart, R.: Recent advances in particle swarm. In: IEEE Congress on Evolutionary Computation, Portland, Oregon, pp. 90–97 (2004)Google Scholar
  23. 23.
    Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computations 8(3), 256–279 (2004)CrossRefGoogle Scholar
  24. 24.
    Srinivasan, D., Seow, T.H.: Particle swarm inspired evolutionary algorithm (PS-EA) for multi-criteria optimization problems. In: Abraham, A., Jain, L.C., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization:Theoretical Advances and Applications. Springer, London (2005)Google Scholar
  25. 25.
    Au, F.T.K., Cheng, Y.S., Tham, L.G., Bai, Z.Z.: Structural damage detection based on a micro-genetic algorithm using incomplete and noisy modal test data. Journal of Sound and Vibration 259(5), 1081–1094 (2003)CrossRefGoogle Scholar
  26. 26.
    Friswell, M.I., Penny, J.E.T., Garvey, S.D.: A combined genetic and eigensensitivity algorithm for the location of damage in structures. Computers and Structures 69, 547–556 (1998)zbMATHCrossRefGoogle Scholar
  27. 27.
    Jaishi, B., Ren, W.X.: Damage detection by finite element model updating using modal flexibility residual. Journal of Sound and Vibration 290, 369–387 (2006)CrossRefGoogle Scholar
  28. 28.
    Ho, S.L., Yang, S., Ni, G., Lo, E.W.C., Wong, H.C.: A particle swarm optimization-based method for multiobjective design optimizations. IEEE Transactions on Magnetics 41(5), 1756–1759 (2005)CrossRefGoogle Scholar
  29. 29.
    Morse, J.N.: Reducing the size of the nondominated set: Pruning by clustering. Computational Operations Research 7(1-2), 55–66 (1980)CrossRefGoogle Scholar
  30. 30.
    Goldberg, D., Richardson, J.J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application, Cambridge, Massachusetts, pp. 41–49 (1987)Google Scholar
  31. 31.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar
  32. 32.
    Agrawal, S., Dashora, Y., Tiwari, M.K., Son, Y.J.: Interactive particle swarm: A Pareto-adaptive metaheuristic to multiobjective optimization. IEEE Transactions on Systems, Man and Cybernetics C Part A: Systems and Humans 38(2), 258–271 (2008)CrossRefGoogle Scholar
  33. 33.
    Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the 1st IEEE Conference on Computation Evolutionary, vol. 1, pp. 82–87 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ricardo Perera
    • 1
  • Sheng-En Fang
    • 1
  1. 1.Department of Structural MechanicsTechnical UniversityMadridSpain

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