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An Improved NSGA-III Algorithm for Reservoir Flood Control Operation

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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.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 51379080, No. 41571514) and the Fundamental Research Funds for the Central Universities (No. 2017KFYXJJ204).

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Authors and Affiliations

  1. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Chen Chen & Xiaohui Yuan

  2. School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Yanbin Yuan

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  1. Chen Chen
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  2. Yanbin Yuan
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  3. Xiaohui Yuan
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Correspondence to Xiaohui Yuan.

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Chen, C., Yuan, Y. & Yuan, X. An Improved NSGA-III Algorithm for Reservoir Flood Control Operation. Water Resour Manage 31, 4469–4483 (2017). https://doi.org/10.1007/s11269-017-1759-6

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  • DOI: https://doi.org/10.1007/s11269-017-1759-6

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