Abstract
Reservoir flood control operation (RFCO) is a multi-objective optimization problem with a long sequence of correlated decision variables. It brings big challenges to large-scale multi-objective optimizers which were generally developed based on the divide-and-conquer strategy. For solving large-scale RFCO problem, a novel coarse-to-fine decomposition method is developed and combined with the algorithmic framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), giving rise to the proposed pCFD-MOEA/D algorithm. The pCFD-MOEA/D algorithm first divides the original RFCO problem into a sequence of sub-problems from coarse to fine scale with different scheduling time intervals. Then all sub-problems are optimized simultaneously and communicate at set intervals. Experimental results on three typical floods at Ankang reservoir have demonstrated that the proposed pCFD-MOEA/D can successfully obtain the elaborate hourly schedule schemes in real time and outperforms the compared algorithms.
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Data Availability
The data set of floods for the Ankang reservoir.
Code Availability
No.
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Funding
This work was supported by the National Natural Science Foundation of China under Grant No. 61772392 and 61976143, the Guangdong Basic and Applied Basic Research Foundation under Grants 2020A151501946 and 2022A1515010139.
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Yang, R., Qi, Y., Lei, J. et al. A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation. Water Resour Manage 36, 3207–3219 (2022). https://doi.org/10.1007/s11269-022-03196-z
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DOI: https://doi.org/10.1007/s11269-022-03196-z