Chaos-Enhanced Harris Hawks Optimizer for Cascade Reservoir Operation with Ecological Flow Similarity

Zhengyang Tang, Shuai Liu, Hui Qin (), Yongchuan Zhang, Xin Zhu, Xiaolin Chen and Pingan Ren
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Zhengyang Tang: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Shuai Liu: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Hui Qin: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yongchuan Zhang: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Xin Zhu: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Xiaolin Chen: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Pingan Ren: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Sustainability, 2025, vol. 17, issue 19, 1-21

Abstract: In the pursuit of sustainable development, optimizing water resources management while maintaining ecological balance is crucial. This study introduces a Chaos-enhanced Harris Hawks Optimizer (CEHHO) aimed at optimizing natural flow patterns in cascade reservoirs. First, an ecological scheduling model considering ensuring guaranteed output is established based on the similarity of ecological flows. Subsequently, the CEHHO algorithm is proposed, which uses tilted skew chaos mapping for population initialization, improving the quality of the initial population. In the exploration phase, an adaptive strategy enhances the efficiency of group search algorithms, enabling effective navigation of the complex solution space. A random difference mutation strategy, combined with the Q-learning algorithm, mitigates premature convergence and maintains algorithmic diversity. Comparative analysis with the existing technology under different typical hydrological frequency shows that the search accuracy and convergence efficiency of the proposed method are significantly improved. Under the guaranteed output limit of 1000 MW, the proposed method enhances the optimal, median, mean, and worst values by 293.92, 493.23, 422.14, and 381.15, respectively, compared to the HHO. Furthermore, the results of the multi-purpose guaranteed output scenario highlight the superior detection and exploitation capabilities of this algorithm. These findings highlight the great potential of the proposed method for practical engineering applications, providing a reliable tool for optimizing water resources management while maintaining ecological balance.

Keywords: ecological scheduling; cascade reservoirs; Harris Hawks Optimizer; skew tent chaotic map; Q-learning algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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