A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation

Rui Yang, Yutao Qi (), Jiaojiao Lei, Xiaoliang Ma and Haibin Zhang
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Rui Yang: Xidian University
Yutao Qi: Xidian University
Jiaojiao Lei: Xidian University
Xiaoliang Ma: Shenzhen University
Haibin Zhang: Xidian University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 9, No 17, 3207-3219

Abstract: 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.

Keywords: Large-scale multi-objective optimization; Reservoir flood control operation; Coarse-to-fine (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03196-z

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