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

Yongqi Liu, Hui Qin (), Li Mo, Yongqiang Wang, Duan Chen, Shusen Pang and Xingli Yin
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2019, vol. 33, issue 1, No 19, 337-354

Abstract: 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.

Keywords: Operation rules; Flood resources utilization; Multi-objective optimization; Decomposition approach; Cultural algorithm (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11269-018-2105-3

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