A Stepwise uncertainty reduction approach to constrained global optimization
International audience Using statistical emulators to guide sequential evaluations of complex computer experiments is now a well-established practice. When a model provides multiple outputs, a typical objective is to optimize one of the outputs with constraints (for instance, a threshold not to exce...
| Main Author: | |
|---|---|
| Other Authors: | , |
| Format: | Conference Object |
| Language: | English |
| Published: |
HAL CCSD
2014
|
| Subjects: | |
| Online Access: | https://hal.inrae.fr/hal-02743553 https://hal.inrae.fr/hal-02743553/document https://hal.inrae.fr/hal-02743553/file/a%20stepwise%20uncertainty%20reduction%20approach%20to%20constrained%20global%20optimization_VP_1.pdf |
| Summary: | International audience Using statistical emulators to guide sequential evaluations of complex computer experiments is now a well-established practice. When a model provides multiple outputs, a typical objective is to optimize one of the outputs with constraints (for instance, a threshold not to exceed) on the values of the other outputs. We propose here a new optimization strategy based on the stepwise uncertainty reduction paradigm, which o ers an e cient trade-off between exploration and local search near the boundaries. The strategy is illustrated on numerical examples. |
|---|