Genetic Combined with a Simplex Algorithm as an Efficient Method for the Detection of a Depressed Ellipsoidal Flaw using the Boundary Element Method

Authors: Clio G. Vossou, Ioannis N. Koukoulis, Christopher G. Provatidis

Abstract:

The present work encounters the solution of the defect identification problem with the use of an evolutionary algorithm combined with a simplex method. In more details, a Matlab implementation of Genetic Algorithms is combined with a Simplex method in order to lead to the successful identification of the defect. The influence of the location and the orientation of the depressed ellipsoidal flaw was investigated as well as the use of different amount of static data in the cost function. The results were evaluated according to the ability of the simplex method to locate the global optimum in each test case. In this way, a clear impression regarding the performance of the novel combination of the optimization algorithms, and the influence of the geometrical parameters of the flaw in defect identification problems was obtained.

Keywords: Defect identification, genetic algorithms, optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333704

References:

[1] T. Burczynski, W. Kus, A. Dlugosza and P. Orantek, "Optimization and defect identification using distributed evolutionary algorithms", Engineering Applications of Artificial Intelligence, vol. 17, 2004, pp. 337-344.
[2] S. Kubo, Inverse analyses and their applications to nondestructive evaluations, in Proc. 12th A-PCNDT 2006 - Asia-Pacific Conference on NDT, Auckland, 2006, pp
[3] S. C. Mellings and M. H. Aliabadi, "Flaw identification using the Boundary Element Method", International Journal For Numerical Methods In Engineering, vol. 38, 1995, pp. 399-419.
[4] C. A. Brebbia and J. Dominguez, Boundary Elements: an introductory course. New York CA: Computational Mechanics Publications, McGraw-Hill Company, 1992.
[5] H. Koguchi and H. Watabe, "Improving defects search in structure by boundary element and genetic algorithm scan method", Engineering Analysis with Boundary Elements, vol. 19, 1997, pp. 105-116.
[6] Y. He, D. Guo and F. Chu, "Using genetic algorithms and finite element methods to detect shaft crack for rotor-bearing system", Mathematics and Computers in Simulation, vol. 57, 2001, pp. 95-108.
[7] M. Shim and M. Suh, "Crack identification using evolutionary algorithms in parallel computing environment", Journal of Sound and Vibration, vol. 262, 2003, pp. 141-160.
[8] A. P. Plumb, R. C. Rowe, P. York and C. Doherty, "Effect of varying optimization parameters on optimization by guided evolutionary simulated annealing (GESA) using a tablet film coat as an example formulation", European Journal of Pharmaceutical Sciences, vol. 18, 2003, pp. 259-266.
[9] M. W. Suh and M. B. Shim, "Crack identification using hybrid Neurogenetic technique", Journal of Sound and Vibration, vol. 238(4), 2000, pp. 617-635.
[10] M. Engelhardt, M. Schanz, G. E. Stavroulakis and H. Antes, "Defect identification in 3-D elastostatics using a genetic algorithm", Optim Eng, vol. 7, 2006 pp. 63-79.
[11] Matlab Optimization toolbox.
[12] A. D. Belegundu and T. R. Chandrupatla, Optimization concepts and applications in engineering. New Jersey, CA: Prentice Hall, 1999.
[13] A.A. Papageorgiou, D.T. Venetsanos and C.G. Provatidis, "Investigating the Influence of Typical Genetic Algorithm Parameters on the Optimization of Benchmark Mathematical Functions", in: D.Tsahalis (ed.), Proc. 2nd International Conference "From Scientific Computing to Computational Engineering", Athens, 2006.
[14] J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright, "Convergence properties of the Nelder-Mead simplex method in low dimensions," SIAM Journal of Optimization, vol. 9(1), 1998, pp.112- 147.
[15] G. E. Stavroulakis and H. Antes, "Nondestructive elastostatic identification of unilateral cracks through BEM and neural networks", Computational Mechanics, vol. 20, 1997, pp. 439-451.