基于协同进化粒子群优化算法的水资源配置模型及应用  

Water Allocation Model Based on Coevolutionary Particle Swarm Optimization Algorithm and Its Application

作　　者：刘洪波[1] 菅浩然[1] LIU Hongbo;JIAN Haoran(Yellow River Conservancy Technical Institute,Kaifeng 475004,China)

机构地区：[1]黄河水利职业技术学院,河南开封475004

出　　处：《人民黄河》2024年第11期74-79,共6页Yellow River

基　　金：河南省自然科学基金资助项目(222300420497)。

摘　　要：面向新发展阶段的城市水资源配置具有多目标、多变量、约束条件复杂、求解结果非线性、求解过程困难等特征。针对线性规划、动态规划、非线性规划等传统优化算法在解决水资源配置问题中求解结果不合理、计算效率低,求解多目标问题收敛慢等问题,提出了基于协同进化粒子群优化(CPSO)算法的多目标水资源优化配置模型。以郑州市为例,构建了以实现社会、经济和生态效益的最大化为目标,供水量、需水量、供水能力和水库库容为约束的水资源配置模型。通过输入郑州市各计算单元和用水部门的用水需求量和可用水量,该模型计算并输出郑州市9个区在2019年、2035年的缺水率。结果表明:郑州市供水的区域分布比较均衡,缺水率在可接受范围内;该模型算法进化速度较快,进化的稳定性较优,优化结果在种群中可以很好地保留且对进化方向的主导性很强,可以有效地应用于解决水资源配置问题,并提升模型计算效率,为水资源管理部门提供技术支持。This paper addressed the characteristics of multi-objective,multi-variable,complex constraints,non-linear solution results and difficult solution process of urban water resources allocation issues in the new development stage.In response to the issues of unreasonable so-lution results,low computational efficiency and slow convergence of traditional optimization algorithms such as linear programming,dynamic programming and non-linear programming in solving water resources allocation issues,a multi-objective water resources allocation model based on coevolutionary particle swarm optimization(CPSO)was proposed.The water resources allocation model was built with the objective of maximising social,economic and ecological benefits,and water supply,water demand,water supply capacity and reservoir capacity by taking Zhengzhou as an example.Through inputting the water demand and water availability of each calculation unit and sector in Zhengzhou,the model calculated and output the proportion of water shortage in the nine districts of Zhengzhou in 2019 and 2035.The results show that the regional distribution of water supply in Zhengzhou City is relatively balanced,and the water shortage rate is within an acceptable range.The algorithm of this model has a fast evolutionary speed and good evolutionary stability.The advantageous results can be well preserved in the population and have strong dominance over the evolutionary direction.It can be effectively applied to solve water resources allocation is-sues and improve the computational efficiency of the model,providing technical support for water resources management departments.

关 键 词：协同进化 粒子群优化算法 水资源优化配置 郑州市 

分 类 号：TV213.4[水利工程—水文学及水资源]