ICF13B

13th International Conference on Fracture June 16–21, 2013, Beijing, China -8- results. Note that these predicted results are computed with the optimized model parameters summarized in table 2. 5. Conclusion The objective of this work is to propose a new strategy to optimize the identification parameters of a micromechanical model coupled with damage [1]. Hence, a combination of genetic algorithm with pattern search algorithm is developed. The genetic algorithm optimizes globally the model parameters; whereas the pattern search algorithm, considered as a local method, has a role to determine the final values of these model coefficients. This model is tested under different cyclic loading complexities. It is recognized that this combination shows its ability to optimize the identification process. Consequently, the predicted responses describe faithfully the experimental results. Acknowledgements The authors are grateful to the AUF and SCAC of the Embassy France in Madagascar for supplying their financial support. References [1] Abdul-Latif, A., and Mounounga T., B. S., (2009), Damage Deactivation Modeling under Multiaxial Cyclic Loadings for Polycrystals, International Journal of Damage Mechanics, 18, 177-198. [2] Dréo J., Pétrowski A., Siarry P,.and Taillard E. (2003), Métaheuristiques pour l’Optimisation Difficile, Eyrolles, ISBN : 2-212-11368-4. [3] Smith R.E., Perelson A.S., and Forrest S., (1993), Searching for diverse, cooperative populations with genetic algorithms, Evolutionary Computation, 1(2), pp.127–149. [4] Christophe Bontemps, Principes Mathématiques et Utilisations des Algorithmes Génétiques, Extrait de cours, Novembre 1995. [5] Kolda, Tamara G., Robert Michael Lewis, and Virginia Torczon. "A generating set direct search augmented Lagrangian algorithm for optimization with a combination of general and linear constraints." Technical Report SAND2006-5315, Sandia National Laboratories, August 2006.

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