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Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition

Author

Listed:
  • Yongqi Liu

    (Huazhong University of Science and Technology)

  • Hui Qin

    (Huazhong University of Science and Technology)

  • Li Mo

    (Huazhong University of Science and Technology)

  • Yongqiang Wang

    (Changjiang River Scientific Research Institute)

  • Duan Chen

    (Changjiang River Scientific Research Institute)

  • Shusen Pang

    (Three Gorges Cascade Dispatch and Communication Center)

  • Xingli Yin

    (Huazhong University of Science and Technology)

Abstract

The operation of a reservoir system for flood resources utilization is a complex problem as it involves many variables, a large number of constraints and multiple objectives. In this paper, a new algorithm named multi-objective cultured evolutionary algorithm based on decomposition (MOCEA/D) is proposed for optimizing the hierarchical flood operation rules (HFORs) with four objectives: upstream flood control, downstream flood control, power generation and navigation. The performance of MOCEA/D is validated through some well-known benchmark problems. On achieving satisfactory performance, MOCEA/D is applied to a case study of HFORs optimization for Three Gorges Project (TGP). The experimental results show that MOCEA/D obtains a uniform non-dominated schemes set. The optimized HFORs can improve the power generation and navigation rate as much as possible under the premise of ensuring flood control safety for small and medium floods (smaller than 1% frequency flood). The obtained results show that MOCEA/D can be a viable alternative for generating multi-objective HFORs for water resources planning and management.

Suggested Citation

  • Yongqi Liu & Hui Qin & Li Mo & Yongqiang Wang & Duan Chen & Shusen Pang & Xingli Yin, 2019. "Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 337-354, January.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:1:d:10.1007_s11269-018-2105-3
    DOI: 10.1007/s11269-018-2105-3
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    References listed on IDEAS

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    1. Benyou Jia & Slobodan P. Simonovic & Pingan Zhong & Zhongbo Yu, 2016. "A Multi-Objective Best Compromise Decision Model for Real-Time Flood Mitigation Operations of Multi-Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3363-3387, August.
    2. M. Reddy & D. Kumar, 2006. "Optimal Reservoir Operation Using Multi-Objective Evolutionary Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 861-878, December.
    3. Hui Qin & Jianzhong Zhou & Youlin Lu & Yinghai Li & Yongchuan Zhang, 2010. "Multi-objective Cultured Differential Evolution for Generating Optimal Trade-offs in 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. 24(11), pages 2611-2632, September.
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