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Multi-swarm fruit fly optimization algorithm for structural damage identification
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 Title & Authors
Multi-swarm fruit fly optimization algorithm for structural damage identification
Li, S.; Lu, Z.R.;
 Abstract
In this paper, the Multi-Swarm Fruit Fly Optimization Algorithm (MFOA) is presented for structural damage identification using the first several natural frequencies and mode shapes. We assume damage only leads to the decrease of element stiffness. The differences on natural frequencies and mode shapes of damaged and intact state of a structure are used to establish the objective function, which transforms a damage identification problem into an optimization problem. The effectiveness and accuracy of MFOA are demonstrated by three different structures. Numerical results show that the MFOA has a better capacity for structural damage identification than the original Fruit Fly Optimization Algorithm (FOA) does.
 Keywords
damage identification;multi-swarm fruit fly optimization algorithm;non-destructive techniques;frequency domain;
 Language
English
 Cited by
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 References
1.
Abolbashari, M.H., Nazari, F. and Rad, J.S. (2014), "A multi-crack effects analysis and crack identification in functionally graded beams using particle swarm optimization algorithm and artificial neural network", Struct. Eng. Mech., 51(2), 299-313. crossref(new window)

2.
Begambrea, O. and Laier, J.E. (2009), "Laiera a hybrid particle swarm optimization-simplex algorithm (PSOS) for structural damage identification", Adv. Eng. Softw., 40(9), 883-891. crossref(new window)

3.
Charalampakis, A.E and Dimou, C.K. (2010), "Identification of Bouc-Wen hystertic systems using particle swarm optimization", Comput. Struct., 88(21-22), 1197-1205. crossref(new window)

4.
Chou, J.H. and Ghaboussi, J. (2001), "Genetic algorithm in structural damage detection", Comput. Struct., 79(14), 1335-1353. crossref(new window)

5.
Dackermann, U., Smith, W.A. and Randall R.B. (2014), "Damage identification based on response-only Measurements using cepstrum analysis and artificial neural network", Struct. Health Monit., 13(4), 430-444. crossref(new window)

6.
Fan, W. and Qiao, P.Z. (2011), "Vibration-based Damage Identification Method: A Review and Comparative Study", Struct. Health Monit., 10(1), 83-110. crossref(new window)

7.
Friswell, M.I., Penny, J.E.T. and Garvey, S.D. (1998), "A combined genetic and eigensensitivity algorithm for the location of damage in structures", Comput. Struct., 69(5), 547-556. crossref(new window)

8.
Guo, H.Y. and Li, Z.L. (2014), "Structural damage identification based on evidence fusion and improved particle swarm optimization", J. Vib. Control, 20(9), 1279-1292. crossref(new window)

9.
Kang, F., Li, J.J. and Xu, Q. (2012), "Damage detection based on improved particle swarm optimization using vibration data", Appl. Soft Comput., 12(8), 2329-2335. crossref(new window)

10.
Kaveh, A. and Zolghadr, A. (2015), "An improved CSS for damage detection of truss structures using changes in natural frequencies and mode shapes", Adv. Eng. Softw., 80, 93-100. crossref(new window)

11.
Kwon, Y.D., Kwon, H.W., Kim, W. and Yeo, S.D. (2008), "Structural damage detection in continuum structures using successive zooming genetic algorithm", Struct. Eng. Mech., 30(2), 135-146. crossref(new window)

12.
Li, J.Q., Pan, Q.K., Mao, K. and Suganthan, P.N. (2014), "Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm", Knowled. Bas. Syst., 72, 28-36. crossref(new window)

13.
Maresa, C. and Suraceb, C. (1996), "An application of genetic algorithm to identify damage in elastic structures", J. Sound Vib., 195(2), 195-215. crossref(new window)

14.
Majumdar, A., De, A., Maity, D. and Maiti, D.K. (2013), "Damage assessment of beams from changes in natural frequencies using ant colony optimization", Struct. Eng. Mech., 45(3), 391-410. crossref(new window)

15.
Mohan, S.C., Yadav, A., Maiti, K.D. and Maity, D. (2014), "A comparative study on crack identification of structures from the changes in natural frequencies using GA and PSO", Eng. Comput., 31(7), 1514-1531. crossref(new window)

16.
Pan, W.T. (2011), Fruit Fly Optimization Algorithm, Tsang Hai Book Publishing, Taiwan.

17.
Pan, W.T. (2012), "A new fruit fly optimization algorithm: taking the financial distress model as an example", Knowled. Bas. Syst., 26, 69-74. crossref(new window)

18.
Shiraz, M.R.N., Mollamahmoudi, H. and Seyedpoor, S.M. (2014), "Structural damage identification using an adaptive multi-stage optimization method based on a modified particle swarm algorithm", J. Optim. Theor. Appl., 160, 1009-1019. crossref(new window)

19.
Tsou, P. and Shen, H.H.H. (1994), "Structural damage detection and identification using neural networks", AIAA J., 32(1), 176-183. crossref(new window)

20.
Yi, T.H., Zhou, G.D., Li, H.N. and Zhang, X.D. (2015), "Optimal sensor placement for health monitoring of high-rise structure based on collaborative-climb monkey algorithm", Struct. Eng. Mech., 54(2), 305-317. crossref(new window)

21.
Yuan, X.F., Dai, X.S., Zhao, J.Y. and He, Q. (2014), "On a novel multi-swarm fruit fly optimazition algorithm and its application", Appl. Math. Comput., 233, 260-271. crossref(new window)