水资源优化配置能够解决区域水资源短缺及不合理利用问题，近年来优化算法成为获得水资源优化配置结果的重要工具。为有效缓解现有优化配置算法中存在的收敛缓慢、局部优化和早熟等问题，介绍了一种新型的元启发式算法：猎豹优化算法（the Cheetah Optimizer，CO），利用该算法收敛速度快、鲁棒性强、寻优能力强等优点，将其运用至水资源优化配置问题中。以太原市为研究区域，建立了以经济效益、社会效益和生态效益为目标的水资源优化配置模型，采用CO求解该模型，并与遗传算法（Genetic Algorithm，GA）结果进行对比分析。结果表明：CO的污染物排放总量在规划年（2030）控制目标内，区域用水满意度达到0.98，能够满足基本用水和发展用水需求；与现状年（2020）相比，生活用水占比下降22.58%，三次产业用水占比分别上升10.53%、25.81%、12.5%，地表水和地下水供水比例分别降低21.70%、11.14%，回用水和引黄水供水比例分别提高了5.43%、27.40%，符合太原市水资源配置思路，验证了CO的有效性；与GA相比，CO的区域满意度、综合用水价值和配水公平性分别提高了7.10%、3.57%和56.52%，污染物排放量和缺水率分别降低了3.11%和3.85%，同时有更好的协调性，验证了CO的优越性。研究成果表明用CO求解水资源优化配置模型合理可行，为水资源优化配置模型求解提供了新方法。

为解决水库优化调度过程中时间尺度不同而导致的水库综合效益不高的问题,以防洪与兴利为目标,将水库长期优化调度与中长期优化调度进行嵌套,建立水库多目标优化调度嵌套模型。其中,多目标问题通过约束法将防洪目标转换为硬性约束后,再转化为求解发电量最大值的单目标问题。算法方面,综合对比后确定长期优化调度采用动态规划算法求解、中长期优化调度采用遗传算法求解。为验证嵌套模型的优化效果,以澄碧河水库为例,进行水库优化模拟调度。结果表明:在满足防洪目标的前提下,嵌套模型优化调度方案相较于长期优化调度方案和实际调度方案效益均更高,验证了嵌套模型在水库优化调度问题中的优越性。

The comprehensive benefit of reservoir is not high due to different time scales in the process of optimal scheduling.In view of this,a nested model of multi-objective optimal scheduling of reservoir is built with both flood control and operational benefit as objectives by nesting the long-term and medium-to-long-term optimal scheduling of reservoir.The multi-objective problem is transformed into a single objective problem of solving the maximum power generation after the flood control objective is transformed into a hard constraint by the constraint method.In terms of algorithm,the long-term optimal scheduling is solved by dynamic programming algorithm,and the medium-to-long-term optimal scheduling is solved by genetic algorithm.The optimization effect of the nested model is verified with Chengbihe reservoir as a case study.The results demonstrate that under the premise of meeting the flood control objectives,the nested model scheme is more effective than the long-term scheme and the actual operation scheme,which verifies the superiority of the nested model.

金沙江下游—三峡已形成六库联合调度格局,调度维数增多,约束庞杂交织,优化目标多样,给调度方案制定带来极大困难。针对传统粒子群算法求解调度模型寻优能力不足的难题,提出多种群引力粒子群算法,建立优化调度模型并应用改进算法求解。算法测试和应用结果表明,多种群引力粒子群算法寻优性能更加先进,更适用于求解梯级水库优化调度问题。实例表明,上游龙头电站通过减少自身发电量可以使下游电站和梯级发电量增加。

The construction and operation of the Three Gorges Project (TGP) have significantly altered the hydrological regime of the middle and lower reaches of the Yangtze River, thereby impacting irrigation water supply. This study focuses on analyzing the effects of TGP operation on the safety of irrigation water supply in the Poyang Lake areas, specifically considering water quantity and fulfillment of water supply. To achieve this, the evolution of water resources and the situation of water supply before and after TGP operation were investigated. Following the operation of TGP, the Poyang Lake witnessed a decreasing trend in available water supply, with the most significant reductions occurring in September and October during impoundment periods. Moreover, the overall water level in the lake area has dropped by 1.1-2.5 meters. Although the total water quantity can still meet irrigation and water supply demands, there is a noticeable decline in the fulfillment of both irrigation and water supply requirements in urban and rural regions, leading to a significant increase in total water shortage. The fulfillment of secured rural and urban water supply and irrigation water supply requirements has dropped from 99.1% and 83.6% before TGP operation to 87.5% and 42.7% after operation, respectively. Simultaneously, water shortage for rural and urban water supply and irrigation water supply has increased from 0.78 and 70.32 to 93.85 and 186.11 million cubic meters, respectively. However, it is worth mentioning that early or delayed impoundment of TGP can slightly mitigate the project’s influence on water supply and irrigation during impoundment, thereby increasing the water supply capacity by 0.2% and 0.3% respectively.

针对经典差分进化算法（DE）的优化性能容易受到变异策略和控制参数影响的问题，提出了一种参数自适应的精英变异差分进化算法（A parameter Adaptive Elite Mutation Eifferential Evolution algorithm，AMEDE）.首先，提出一种精英变异策略的方法，其目的是为了方便获取优秀个体信息；其次，引入新的控制参数，使得算法可以在更大的搜索空间进行搜索；最后，利用自适应参数学习方法，为种群中的每个个体赋予不同的控制参数值，并根据种群多样性和精英个体的信息动态更新个体的参数，使算法避免过早的收敛并提高算法的收敛精度.对本文提出的AMEDE算法与其他6种改进差分进化算法（DE，CoDE，JaDE，JDE，SaDE，GPDE）在16个基准测试函数上进行了三组对比实验.实验结果表明，AMEDE算法在高维函数和低维函数上都具有搜索精度高、收敛速度快和鲁棒性强等优点.

Aiming at the problem that the optimization performance of classical differential evolution algorithm（DE）is easily affected by mutation strategy and control parameters，a parameter adaptive elite mutation differential evolution algorithm（AMEDE）is proposed.Firstly，a method of elite mutation strategy is proposed，which aims to facilitate the acquisition of excellent individual information；Secondly，new control parameters are introduced to make the algorithm search in a larger search space；Finally，using the adaptive parameter learning method，each individual in the population is given different control parameter values，and the individual parameters are dynamically updated according to the population diversity and the information of elite individuals，so as to avoid premature convergence and improve the convergence accuracy of the algorithm.The AMEDE algorithm proposed in this paper is compared with six other improved differential evolution algorithms（DE，CoDE，JaDE，JDE，SaDE，GPDE）on 16 benchmark functions.Experimental results show that AMEDE algorithm has the advantages of high search accuracy，fast convergence speed and strong robustness in both highdimensional and lowdimensional functions.