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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

Author

Listed:
  • Rui Yang

    (Xidian University)

  • Yutao Qi

    (Xidian University)

  • Jiaojiao Lei

    (Xidian University)

  • Xiaoliang Ma

    (Shenzhen University)

  • Haibin Zhang

    (Xidian University)

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.

Suggested Citation

  • Rui Yang & Yutao Qi & Jiaojiao Lei & Xiaoliang Ma & Haibin Zhang, 2022. "A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3207-3219, July.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:9:d:10.1007_s11269-022-03196-z
    DOI: 10.1007/s11269-022-03196-z
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    References listed on IDEAS

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