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（improved multi-objective cuckoo search algorithm，IMOCS）。针对多目标布谷鸟优化算法（multi-objective cuckoo search algorithm，MOCS）收敛速度慢、容易陷入局部最优解的缺点，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2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0.21亿、0.51亿和0.07亿m3，损失水量分别减少了0.13亿、1.53亿和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。

For inter-basin water transfer projects, the water conflict between society, economy, ecology, and other sectors makes the water allocation process more complicated. The optimal allocation of water resources was one of the most effective ways to alleviate the conflict between water supply and demand. It was necessary to implement an effective water resources optimization allocation concept in inter-basin water transfer projects to ensure the efficient implementation of inter-basin water transfer projects. This study presents an improved multi-objective cuckoo algorithm to effectively solve the multi-objective optimal allocation problem of inter-basin water resources. To address the shortcomings of multi-objective cuckoo algorithm such as slow convergence speed and easy to fall into local optimal solutions, chaos theory and variation mechanism were introduced, and adaptive discovery probability and step size were used to improve multi-objective cuckoo algorithm and enhance the overall performance of improved multi-objective cuckoo algorithm. A water resources optimal allocation model of the Jiangsu section of the South-to-North Water Transfers Project was established. Improved multi-objective cuckoo algorithm and multi-objective cuckoo algorithm were adopted to solve the water resources optimal allocation model, respectively. A non-negative matrix factorization method based on combined weighting was used for scheme evaluation. The results show that: improved multi-objective cuckoo algorithm was better than multi-objective cuckoo algorithm in terms of convergence, distribution, and overall performance, and was capable of yielding higher-quality Pareto solution sets; Compared with the optimal allocation solution solved by multi-objective cuckoo algorithm under the 50%, 75%, and 95% incoming water conditions, the total water shortage of the optimal allocation solution solved by improved multi-objective cuckoo algorithm was reduced by 21 million m3, 51 million m3 and 7 million m3, and the water loss was reduced by 13 million m3, 15 million m3 and 11 million m3, respectively. Therefore, improved multi-objective cuckoo algorithm could provide an effective algorithmic reference for the calculation of multi-objective optimal allocation of inter-basin water resources.