Multicore Parallel Genetic Algorithm with Tabu Strategy for Rainfall-Runoff Model Calibration
Publication: Journal of Hydrologic Engineering
Volume 22, Issue 8
Abstract
Conceptual rainfall-runoff models (CRRMs) are widely used for flood forecasting and hydrologic simulations. However, parameter calibration poses a major challenge for using CRRMs, especially given that climate changes and effects of human activities often necessitate recalibration of CRRMs. Genetic algorithms (GAs) are one of the most widely used optimization techniques for hydrological model calibration, and have been widely and successfully used in model calibration; however, the complexity and high dimensionality of parameter calibration make them time-consuming and prone to local optima. Moreover, repetitive computation of fitness values in the GAs greatly reduces the efficiency. Therefore, new methods must be explored to improve the computational efficiency. Multicore parallel technology, which enables resource sharing and has low parallel costs and computing burdens, offers considerable benefits for parameter calibration. Thus, a multicore parallel genetic algorithm (MCPGA) based on the fork–join parallel framework with a tabu strategy is proposed in this paper for CRRM calibration. The method uses a multicore to divide the original task into several small subtasks and complete them concurrently, and adopts tabu strategy to avoid redundant computation in the GA to enhance the computing efficiency. The current methodology is applied to parameter calibration for the Xinanjiang model (which is a typical CRRM and has extensive applications in humid and semi-humid regions in China) of the Shuangpai Reservoir. Result comparisons between the MCPGA and serial GA indicate that the proposed method ensures a high degree of accuracy and significantly improves the computational efficiency.
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Acknowledgments
This study is supported by the National Natural Science Foundation of China (Nos. 51209029 and 51210014) and the Fundamental Research Funds for the Central Universities (No. DUT16TD10).
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Journal of Hydrologic Engineering
Volume 22 • Issue 8 • August 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Jul 17, 2016
Accepted: Mar 3, 2017
Published online: Jun 1, 2017
Published in print: Aug 1, 2017
Discussion open until: Nov 1, 2017
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