Name
Title | ||
|---|---|---|
Data-Driven Adaptive Probabilistic Robust Optimization Using Information Granulation. |
Abstract | ||
|---|---|---|
In this paper, we consider a generic class of adaptive optimization problems under uncertainty, and develop a data-driven paradigm of adaptive probabilistic robust optimization (APRO) in a robust and computationally tractable manner. The paradigm comprises two phases: 1) bilayer information granulation (IG), which involves the data-mining techniques and nested decomposition of convex sets that est... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCYB.2016.2638461 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Optimization,Robustness,Computational modeling,Adaptation models,Uncertainty,Stochastic processes,Data models | Mathematical optimization,Probabilistic-based design optimization,Stochastic optimization,Adaptive optimization,Robust optimization,Test functions for optimization,Robustness (computer science),Artificial intelligence,Probabilistic logic,Stochastic programming,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
48 | 2 | 2168-2267 |
Citations | PageRank | References |
1 | 0.35 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuming Wang | 1 | 229 | 15.96 |
| W. Pedrycz | 2 | 13966 | 1005.85 |