为有效求解跨流域水资源多目标优化调配问题,提出一种改进多目标布谷鸟算法(IMOCS)。针对MOCS存在收敛速度慢,容易陷入局部最优解等缺点,引入混沌理论和变异机制,采用自适应发现概率和步长改进MOCS,形成IMOCS算法。以南水北调东线工程江苏段为例,构建跨流域水资源多目标调配模型,分别采用IMOCS和MOCS求解模型,并运用基于组合赋权的非负矩阵分解法对两种算法所得的Pareto解集进行评价。结果表明:IMOCS在收敛性、多样性和综合性能方面优于MOCS,能够得到更高质量的Pareto解集；相较于50%、75%和95%来水频率下的MOCS所求解的最优配置方案,IMOCS所求解的最优配置方案缺水总量减少了0.21亿m3、0.51亿m3和0.07亿m3,损失水量分别减少了0.13亿m3、1.53亿m3和1.11亿m3。因此,IMOCS可为跨流域水资源多目标优化配置计算提供有效的算法参考。

This study presents an improved multi-objective cuckoo algorithm (IMOCS) to effectively solve the multi-objective optimal allocation problem of inter-basin water resources. To address the shortcomings of MOCS such as slow convergence speed and easy to fall into local optimal solutions, chaos theory and variation mechanism are introduced, and adaptive discovery probability and step size are used to improve MOCS and enhance the overall performance of IMOCS. A water resources optimal allocation model of the Jiangsu section of the South-to-North Water Transfer (J-SNWT) Project is established. IMOCS and MOCS are adopted to solve the water resources optimal allocation model, respectively. A non-negative matrix factorization method based on combined weighting is used for scheme evaluation. The results show that: IMOCS is better than MOCS in terms of convergence, distribution, and overall performance, and is capable of yielding higher-quality Pareto solution sets; Compared with the optimal allocation solution solved by MOCS under the 50%, 75%, and 95% incoming water conditions, the total water shortage of the optimal allocation solution solved by IMOCS is reduced by 21 million m3, 51 million m3 and 7 million m3, and the water loss is reduced by 13 million m3, 15 million m3 and 11 million m3, respectively. Therefore, IMOCS can provide an effective algorithmic reference for the calculation of multi-objective optimal allocation of inter-basin water resources.

江苏省高效节能大型轴流泵站工程研究中心开放课题(ECHEAP013)；国家自然科学基金(52009116)；江苏省自然科学基金(BK20200959；BK20200958)；中国博士后科学基金(2018M642338)