Name
Title | ||
|---|---|---|
Improving Ordinal Transformation Through Optimal Combination Of Multi-Model Predictions |
Abstract | ||
|---|---|---|
Optimization of large-scale complex systems often involves high-fidelity computational simulation models that are very time-consuming. As a result, the number of objective function evaluations is often very limited and presents a major hurdle for optimization. Previous works on a new framework known as ordinal transformation (OT) provides a method that makes use of a low-fidelity approximate model to speed up optimization. The effectiveness of OT depends crucially on the accuracy of the predictions by the approximate model. In this paper, we study how to improve the quality of the predictions when there are two or more low-fidelity models. We set up an optimization formulation that allows us to identify the optimal linear combination of multiple low-fidelity model outputs to improve the quality of the prediction. Preliminary numerical experiments demonstrate that the new method is very effective and can lead to significant improvement. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICIT.2016.7474990 | PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT) |
Keywords | Field | DocType |
optimization, ordinal transformation, simulation, fidelity level | Complex system,Computational simulation,Linear combination,Mathematical optimization,Numerical models,Ordinal number,Computer science,Speedup | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Si Zhang | 1 | 49 | 6.18 |
| Jie Xu | 2 | 81 | 11.71 |
| Edward Huang | 3 | 64 | 7.87 |
| Chun-Hung Chen | 4 | 21 | 6.85 |
| Siyang Gao | 5 | 80 | 11.83 |