Water Resources Management

, Volume 33, Issue 1, pp 337–354 | Cite as

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

  • Yongqi Liu
  • Hui QinEmail author
  • Li Mo
  • Yongqiang Wang
  • Duan Chen
  • Shusen Pang
  • Xingli Yin


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.


Operation rules Flood resources utilization Multi-objective optimization Decomposition approach Cultural algorithm 



This work is supported by the National Natural Science Foundation of China (No. 91647114, No. 51779013, 51479075), the Natural Science Foundation of Hubei Province (2017CFB613), the Fundamental Research Funds for the Central Universities (HUST: 2016YXZD047), and special thanks are given to the anonymous reviewers and editors for their constructive comments.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.School of Hydropower and Information EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.Changjiang River Scientific Research InstituteWuhanChina
  3. 3.Three Gorges Cascade Dispatch and Communication CenterYichangChina

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