The spreading of advanced constituive models, needed to model complex phenomena, makes necessary to solve difficult parameter identification problems. The need of multiple tests to fully characterize the experimental behaviour makes the parameter identification problem a multi objective one. Unlike conventional techniques, based on the formulation of an aggregate scalar ob- jective function, in the present work the problem is addressed using a new multi objective algorithm obtained extending the continuous Ant Colony Optimization algorithm. Mathematical tests and ap- plication to a real world problem are performed and different performance measures are used to asses the performance of the approach.
|Numero di pagine||0|
|Stato di pubblicazione||Published - 2009|