Affiliation:
1. Northeastern University
2. University of Jinan
Abstract
It has gained some popularity that optimization methods are used for identification of material parameters, furthermore because of non-linear relationship between identified parameters and foregone information, mostly parameter identification problem must be expressed in terms of a global optimization problem. In order to solve successfully non-linear parameter identification problem, a new global optimization algorithm, which is based on the general dynamic canonical descent method, is proposed. The results in numerical experiments and engineering application both show that the proposed method will be robust one in the field of non-linear parameter identification.
Publisher
Trans Tech Publications, Ltd.
Reference8 articles.
1. K. Bousson, S.D. Correia. Optimization algorithm based on densification and dynamic canonical descent. Journal of Computational and Applied mathematics, 191(2006), 269-279.
2. K. Bousson. Efficient global optimization based on dynamic canonical descent. Systems Sci. 26(4) (2000), 61-78.
3. D.E. Goldberg. Genetic Algorithm in Search, Optimization&Machine Learning, Addison-Wesley, (1989).
4. S. Kirpatrick, C.D. Gelatt, M.P. Vecchi. Optimization by simulate annealing. Science, 220 (1983), 671-680.
5. Zafer Bingul. Adaptive genetic algorithms applied to dynamic multiobjective problems. Applied Soft Computing. 7 (2007), 791-799.