Wang Zheng, Dai Rui, Wang Wanliang, Jie Jing, Ye Qianlin
College of Computer Science and Technology, Hangzhou City University, Hangzhou, 310015, China.
School of Computer and computational Sciences, Zhejiang University of Technology, Hangzhou, 310023, China.
Sci Rep. 2025 Jul 1;15(1):20523. doi: 10.1038/s41598-025-06385-z.
With the intensification of global climate change and increasing anthropogenic pressures, effective water resource management has become a critical challenge for sustainable development. Small watershed cascade reservoirs must balance multiple competing objectives, including flood control, hydropower generation, and water supply for ecological, agricultural, and industrial uses. This study develops a many-objective optimization scheduling model for cascade reservoirs in the Lushui River Basin and proposes a constrained many-objective evolutionary multitasking optimization algorithm (EMCMOA) to solve it. The algorithm incorporates a dual-task structure with dynamic knowledge transfer to improve search efficiency and solution quality. Experimental results show that EMCMOA outperforms several state-of-the-art algorithms on benchmarks and real-world scenarios, achieving up to 15.7% improvement in IGD and 12.6% increase in HV. Furthermore, EMCMOA demonstrates strong adaptability to varying hydrological conditions, providing reliable and adaptive scheduling strategies. These results highlight its capability to support flexible, many-objective trade-offs in real-world water resource management.
随着全球气候变化的加剧和人为压力的增加，有效的水资源管理已成为可持续发展的一项关键挑战。小流域梯级水库必须平衡多个相互竞争的目标，包括防洪、水力发电以及为生态、农业和工业用途供水。本研究开发了泸水河流域梯级水库的多目标优化调度模型，并提出了一种约束多目标进化多任务优化算法（EMCMOA）来求解该模型。该算法采用具有动态知识转移的双任务结构，以提高搜索效率和解决方案质量。实验结果表明，EMCMOA在基准测试和实际场景中优于几种先进算法，在IGD上提高了15.7%，在HV上提高了12.6%。此外，EMCMOA对不同水文条件具有很强的适应性，提供了可靠且自适应的调度策略。这些结果凸显了其在实际水资源管理中支持灵活的多目标权衡的能力。
