Name
Title | ||
|---|---|---|
A multi-core CPU and many-core GPU based fast parallel shuffled complex evolution global optimization approach. |
Abstract | ||
|---|---|---|
In the field of hydrological modelling, the global and automatic parameter calibration has been a hot issue for many years. Among automatic parameter optimization algorithms, the shuffled complex evolution developed at the University of Arizona (SCE-UA) is the most successful method for stably and robustly locating the global “best” parameter values. Ever since the invention of the SCE-UA, the profession suddenly has a consistent way to calibrate watershed models. However, the computational efficiency of the SCE-UA significantly deteriorates when coping with big data and complex models. For the purpose of solving the efficiency problem, the recently emerging heterogeneous parallel computing (parallel computing by using the multi-core CPU and many-core GPU) was applied in the parallelization and acceleration of the SCE-UA. The original serial and proposed parallel SCE-UA were compared to test the performance based on the Griewank benchmark function. The comparison results indicated that the parallel SCE-UA converged much faster than the serial version and its optimization accuracy was the same as the serial version. It has a promising application prospect in the field of fast hydrological model parameter optimization. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TPDS.2016.2575822 | IEEE Trans. Parallel Distrib. Syst. |
Keywords | Field | DocType |
Optimization,Water resources,Algorithm design and analysis,Parallel processing,Computational modeling,Calibration,Genetic algorithms | Hydrological modelling,Algorithm design,Global optimization,Computer science,Parallel computing,Meta-optimization,Acceleration,Multi-core processor,Big data,Genetic algorithm | Journal |
Volume | Issue | ISSN |
28 | 2 | 1045-9219 |
Citations | PageRank | References |
2 | 0.39 | 11 |
Authors | ||
13 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guang-Yuan Kan | 1 | 20 | 1.89 |
| Tianjie Lei | 2 | 2 | 2.08 |
| Ke Liang | 3 | 6 | 1.51 |
| Jiren Li | 4 | 11 | 4.13 |
| Liuqian Ding | 5 | 2 | 0.73 |
| Xiaoyan He | 6 | 6 | 1.17 |
| Haijun Yu | 7 | 2 | 0.73 |
| Dawei Zhang | 8 | 2 | 0.39 |
| Depeng Zuo | 9 | 3 | 1.43 |
| Zhenxin Bao | 10 | 3 | 2.11 |
| Mark Amo-Boateng | 11 | 2 | 0.39 |
| Youbing Hu | 12 | 5 | 0.81 |
| Mengjie Zhang | 13 | 3777 | 300.33 |