Name
Abstract | ||
|---|---|---|
Accurate parameter estimation for the Muskingum method is important in its use for forecasting flood damage. Combining ideas from quantum computing and evolutionary computing, a quantum genetic algorithm (QGA) is presented for this purpose, which is shown to offer higher precision and greater robustness than eight typical methods. However, it requires longer computation time. In this paper, we therefore provide an implementation of a parallel quantum genetic algorithm (PQGA) in C-CUDA for estimating the parameters of the Muskingum model. PQGA was tested on a classic river example from China. Experimental results show that the PQGA scales well with available computing resources and is substantially faster than QGA and the other methods. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) | Quantum computing, genetic algorithm, GPU, parallel algorithm, Muskingum model |
Field | DocType | Citations |
Mathematical optimization,Computer science,Quantum genetic algorithm,Quantum computer,Algorithm,Evolutionary computation,Robustness (computer science),Estimation theory,Genetic algorithm,Computation | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aijia Ouyang | 1 | 159 | 19.34 |
| Martin H. Luerssen | 2 | 82 | 10.29 |
| Qian Wang | 3 | 245 | 55.19 |
| Xuyu Peng | 4 | 7 | 3.13 |