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

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Vydáno v:Water Resources Management vol. 36, no. 9 (Jul 2022), p. 3207
Hlavní autor: Yang, Rui
Další autoři: Qi, Yutao, Lei, Jiaojiao, Ma, Xiaoliang, Zhang, Haibin
Vydáno:
Springer Nature B.V.
Témata:
Scheduling
Algorithms
Flood control
Optimization
Decomposition
Reservoirs
Multiple objective analysis
Real time
Sequencing
Intervals
Floods
Evolutionary algorithms
Dams
Computer science
Dynamic programming
Genetic algorithms
Variables
Linear programming
Optimization algorithms
Economic
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Abstrakt: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.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-022-03196-z
Zdroj:ABI/INFORM Global