Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
On the Design of Optimization Strategies Based on Global Response Surface Approximation Models
Journal of Global Optimization
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Journal of Global Optimization
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Multidisciplinary design optimization of mechanisms
Advances in Engineering Software
Mathematics and Computers in Simulation
Multiparametric analysis within the proper generalized decomposition framework
Computational Mechanics
Generation of a cokriging metamodel using a multiparametric strategy
Computational Mechanics
A multiparametric strategy for the two step optimization of structural assemblies
Structural and Multidisciplinary Optimization
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The main objective of this paper is to propose an optimization strategy which uses partially converged data to minimize the computational effort associated with an optimization procedure. The framework of this work is the optimization of assemblies involving contact and friction. Several tools have been developed in order to use a surrogate model as an alternative to the actual mechanical model. Then, the global optimization can be carried out using this surrogate model, which is much less expensive. This approach has two drawbacks: the CPU time required to generate the surrogate model and the inaccuracy of this model. In order to alleviate these drawbacks, we propose to minimize the CPU time by using partially converged data and then to apply a correction strategy. Two methods are tested in this paper. The first one consists in updating a partially converged metamodel using global enrichment. The second one consists in seeking the global minimum using the weighted expected improvement. One can achieve a time saving of about 10 when seeking the global minimum.