Ata uhi

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

I tiakina i:
I whakaputaina i:Water Resources Management vol. 36, no. 9 (Jul 2022), p. 3207
Kaituhi matua: Yang, Rui
Ētahi atu kaituhi: Qi, Yutao, Lei, Jiaojiao, Ma, Xiaoliang, Zhang, Haibin
I whakaputaina:
Springer Nature B.V.
Ngā marau:
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
Urunga tuihono:Citation/Abstract
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Whakarāpopotonga: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
Puna:ABI/INFORM Global