TY - CONF
T1 - Multi-objective parameter identification via ACOR algorithm
AU - Rizzo, Santi
AU - Fileccia Scimemi, Giuseppe
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - ACOR
KW - multi-objective optimization.
KW - parameters identification
KW - ACOR
KW - multi-objective optimization.
KW - parameters identification
UR - http://hdl.handle.net/10447/42027
M3 - Other
ER -