Abstract
In solving non-linear inverse optimization problems, how to make sure one and only solution and cut down computational effort are two very representative challenges. As thus, the derivative-free filled function method is proposed, it couples the classical filled method theory and dynamic canonical descent method. The application results of non-linear inverse analysis of material parameters for a practical engineering show that the derivative-free filled function method can quickly solve non-linear inverse problems with one and only solution, and it will be robust one in non-linear inverse solution field.
Publisher
Trans Tech Publications, Ltd.
Reference9 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.