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A trajectory-based sampling strategy for sequentially refined metamodel management of metamodel-based dynamic optimization in mechatronics

Tom Lefebvre (UGent) , Frederik De Belie (UGent) and Guillaume Crevecoeur (UGent)
Author
Organization
Abstract
Dynamic optimization problems based on computationally expensive models that embody the dynamics of a mechatronic system can result in prohibitively long optimization runs. When facing optimization problems with static models, reduction in the computational time and thus attaining convergence can be established by means of a metamodel placed within a metamodel management scheme. This paper proposes a metamodel management scheme with a dedicated sampling strategy when using computationally demanding dynamic models in a dynamic optimization problem context. The dedicated sampling strategy enables to attain dynamically feasible solutions where the metamodel is locally refined during the optimization process upon satisfying a feasibility-based stopping condition. The samples are distributed along the iterate trajectories of the sequential direct dynamic optimization procedure. Algorithmic implementation of the trajectory-based metamodel management is detailed and applied on two case studies involving dynamic optimization problems. These numerical experiments illustrate the benefits of the presented scheme and its sampling strategy on the convergence properties. It is shown that the acceleration of the solution time of the dynamic optimization problem can be achieved when evaluating the metamodel that is lower than 90% compared to the computationally expensive model.
Keywords
metamodel-based optimization, metamodel management, sequential dynamic optimization

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Please use this url to cite or link to this publication:

Chicago
Lefebvre, Tom, Frederik De Belie, and Guillaume Crevecoeur. 2018. “A Trajectory-based Sampling Strategy for Sequentially Refined Metamodel Management of Metamodel-based Dynamic Optimization in Mechatronics.” Optimal Control Applications & Methods .
APA
Lefebvre, T., De Belie, F., & Crevecoeur, G. (2018). A trajectory-based sampling strategy for sequentially refined metamodel management of metamodel-based dynamic optimization in mechatronics. OPTIMAL CONTROL APPLICATIONS & METHODS .
Vancouver
1.
Lefebvre T, De Belie F, Crevecoeur G. A trajectory-based sampling strategy for sequentially refined metamodel management of metamodel-based dynamic optimization in mechatronics. OPTIMAL CONTROL APPLICATIONS & METHODS . Wiley; 2018;
MLA
Lefebvre, Tom, Frederik De Belie, and Guillaume Crevecoeur. “A Trajectory-based Sampling Strategy for Sequentially Refined Metamodel Management of Metamodel-based Dynamic Optimization in Mechatronics.” OPTIMAL CONTROL APPLICATIONS & METHODS (2018): n. pag. Print.
@article{8562380,
  abstract     = {Dynamic optimization problems based on computationally expensive models that embody the dynamics of a mechatronic system can result in prohibitively long optimization runs. When facing optimization problems with static models, reduction in the computational time and thus attaining convergence can be established by means of a metamodel placed within a metamodel management scheme. This paper proposes a metamodel management scheme with a dedicated sampling strategy when using computationally demanding dynamic models in a dynamic optimization problem context. The dedicated sampling strategy enables to attain dynamically feasible solutions where the metamodel is locally refined during the optimization process upon satisfying a feasibility-based stopping condition. The samples are distributed along the iterate trajectories of the sequential direct dynamic optimization procedure. Algorithmic implementation of the trajectory-based metamodel management is detailed and applied on two case studies involving dynamic optimization problems. These numerical experiments illustrate the benefits of the presented scheme and its sampling strategy on the convergence properties. It is shown that the acceleration of the solution time of the dynamic optimization problem can be achieved when evaluating the metamodel that is lower than 90\% compared to the computationally expensive model.},
  author       = {Lefebvre, Tom and De Belie, Frederik and Crevecoeur, Guillaume},
  issn         = {0143-2087},
  journal      = {OPTIMAL CONTROL APPLICATIONS \& METHODS },
  keyword      = {metamodel-based optimization,metamodel management,sequential dynamic optimization},
  language     = {eng},
  publisher    = {Wiley},
  title        = {A trajectory-based sampling strategy for sequentially refined metamodel management of metamodel-based dynamic optimization in mechatronics},
  url          = {http://dx.doi.org/10.1002/oca.2442},
  year         = {2018},
}

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