Yu et al. (2026) Improved ε -constrained and adaptive hybrid crossover operator-based NSGA-III for reservoir multi-objective ecological operation

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-01-07
• Authors: Yi Yu, Xuefeng Min, Tianyu Zhou, Wenjie Xu, Rui Zhao, Ligang Deng, Lianghan Zhu, SHI Yang
• DOI: 10.1016/j.jhydrol.2026.134929

Research Groups

• School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, China
• Eco-Environmental Science Research & Design Institute of Zhejiang Province, Hangzhou, China

Short Summary

This study proposes an integrated multi-objective ecological operation model for reservoirs, considering power generation, flood control, ecological flow, and fish reproduction, and develops an improved NSGA-III algorithm (ε-AHCGA) that demonstrates superior performance compared to state-of-the-art constrained multi-objective optimization algorithms.

Objective

• To develop an integrated multi-objective ecological operation model for reservoirs that comprehensively considers power generation, flood control, ecological flow maintenance, and hydrological process requirements for fish reproduction.
• To propose and evaluate ε-AHCGA, an improved NSGA-III algorithm incorporating ε-constraints and an adaptive hybrid crossover operator, for solving complex multi-objective ecological operation problems.
• To analyze the relationships between objective functions using Pareto-optimal solutions and provide recommendations for enhanced reservoir management practices based on optimized operation schemes.

Study Configuration

• Spatial Scale: General multi-purpose reservoir systems; algorithm evaluated on 47 benchmark test problems and applied to an unspecified reservoir for comparative assessment.
• Temporal Scale: Operational planning for reservoir management, including comparative assessment with actual historical operations.

Methodology and Data

• Models used:
  • Integrated multi-objective ecological operation model for reservoirs (considering power generation, flood control, ecological flow, fish reproduction).
  • ε-AHCGA: An improved NSGA-III algorithm incorporating ε-constraints and an adaptive hybrid crossover operator.
• Data sources:
  • 47 benchmark test problems (for algorithm performance evaluation).
  • Data from actual historical reservoir operations (for comparative assessment).

Main Results

• An integrated multi-objective ecological operation model was proposed, balancing power generation, flood control, ecological flow, and fish reproduction requirements.
• The ε-AHCGA algorithm, incorporating ε-constraints and an adaptive hybrid crossover operator, was developed to enhance the ability to traverse infeasible regions and maintain population diversity.
• ε-AHCGA demonstrated superior competitiveness compared to five state-of-the-art constrained multi-objective optimization algorithms when evaluated on 47 benchmark problems and the proposed reservoir operation model.
• Pareto-optimal solutions obtained by ε-AHCGA were used to analyze the relationships between the model’s objective functions.
• A comparative assessment between optimized operation schemes and actual historical operations led to specific recommendations for improved reservoir management practices.

Contributions

• Development of a comprehensive multi-objective ecological operation model for reservoirs that integrates diverse objectives including power generation, flood control, ecological flow, and fish reproduction.
• Introduction of ε-AHCGA, an enhanced NSGA-III algorithm, which significantly improves performance in constrained multi-objective optimization by incorporating ε-constraints and an adaptive hybrid crossover operator.
• Design of a novel adaptive hybrid crossover operator that effectively increases algorithmic diversity without compromising convergence.
• Provision of a robust decision-support tool for sustainable water resource management in complex reservoir systems, validated through rigorous testing and comparative assessment.

Funding

Not specified in the provided text.

Citation

@article{Yu2026Improved,
  author = {Yu, Yi and Min, Xuefeng and Zhou, Tianyu and Xu, Wenjie and Zhao, Rui and Deng, Ligang and Zhu, Lianghan and Yang, SHI},
  title = {Improved <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si8.svg"> <mml:mrow> <mml:mi mathvariant="bold-italic">ε</mml:mi> </mml:mrow> </mml:math> -constrained and adaptive hybrid crossover operator-based NSGA-III for reservoir multi-objective ecological operation},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.134929},
  url = {https://doi.org/10.1016/j.jhydrol.2026.134929}
}