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An Improved NSGA-III Algorithm for Reservoir Flood Control Operation

Author

Listed:
  • Chen Chen

    (Huazhong University of Science and Technology)

  • Yanbin Yuan

    (Wuhan University of Technology)

  • Xiaohui Yuan

    (Huazhong University of Science and Technology)

Abstract

An improved non-dominated sorting genetic algorithm-III (ENSGA-III) is proposed to solve reservoir flood control operation (RFCO) problem. The highest upstream water level and the largest discharge of the dam are considered as two objective functions for the RFCO problem. In the proposed ENSGA-II, there are three aspects of improvements in the original NSGA-III. First, orthogonal design is adopted to generate initial population for making the population more spread and uniform in the search space. Secondly, ε-dominance and constraint violation strategy is designed to find the non-dominated solution set. Thirdly, double populations are updated with three-archive strategy for producing better individuals in evolutionary process. The performance of the proposed ENSGA-III has been tested on the RFCO problem of Three Gorges Reservoir. The simulation results show that the proposed method is able to produce well distributed Pareto optimal solutions for the multi-objective reservoir flood control operation problem in term of solution quality. Compared with the results obtained by other methods, the superiority of the ENSGA-III for solving the RFCO problem is verified.

Suggested Citation

  • Chen Chen & Yanbin Yuan & Xiaohui Yuan, 2017. "An Improved NSGA-III Algorithm for 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. 31(14), pages 4469-4483, November.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:14:d:10.1007_s11269-017-1759-6
    DOI: 10.1007/s11269-017-1759-6
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    References listed on IDEAS

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    1. Daniel Che & Larry Mays, 2015. "Development of an Optimization/Simulation Model for Real-Time Flood-Control Operation of River-Reservoirs Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 3987-4005, September.
    2. Po-Kuan Chiang & Patrick Willems, 2015. "Combine Evolutionary Optimization with Model Predictive Control in Real-time Flood Control of a River System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2527-2542, June.
    3. D. Nagesh Kumar & Falguni Baliarsingh & K. Srinivasa Raju, 2010. "Optimal Reservoir Operation for Flood Control Using Folded Dynamic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1045-1064, April.
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    Cited by:

    1. Dariane, A.B. & Ghasemi, M. & Karami, F. & Azaranfar, A. & Hatami, S., 2021. "Crop pattern optimization in a multi-reservoir system by combining many-objective and social choice methods," Agricultural Water Management, Elsevier, vol. 257(C).

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