遗传算法和粒子群算法求解渠系多目标优化模型  

Genetic Algorithm and PSO Algorithm for Solving Canal System Multiobjective Optimization Model Canal

作　　者：褚宏业 王莹[1] 文俊[1] 李依耘 杨鑫[1] 

机构地区：[1]云南农业大学水利学院,昆明650201

出　　处：《中国农村水利水电》2015年第4期9-11,17,共4页China Rural Water and Hydropower

基　　金：国家自然科学基金项目(51349010);国家重点实验室开放基金项目(2012490311);云南省教育厅基金项目(2012Y373);国家科技支撑计划项目(2012BAD40B00)

摘　　要：目前高原灌区灌溉用水管理相对落后,渠系渗漏损失较大、渠道闸门运行调度不合理等问题十分突出。因此,开展渠系工作制度优化的研究,对缓解灌区用水供需矛盾有着重要意义。考虑不同时刻各配水渠道流量标准差最小和各配水渠道平均日渗漏损失最小两个目标函数,以配水连续性和灌溉可供水量作为约束条件,建立渠系工作制度多目标优化模型。以云南省蜻蛉河灌区为例,采用遗传算法和粒子群算法进行优化求解。结果表明,遗传算法和粒子群算法都可以解决渠系工作制度优化问题,对各渠道闸门开关时间进行优化,能够满足灌区需求,从而减少渠系渗漏损失,达到节约灌溉用水量的目的。两种算法相比,粒子群算法运算速度更快,优化闸门运行调度更合理。Currently irrigation water management is relatively backward plateau .irrigation district .A larger canal system seepage loss ,the channel floodgate operation scheduling problem are very unreasonable .Therefore ,this paper considers channels of different moments each with water flow rate standard differential and each with a minimum average daily water seepage channels of minimal loss .Continuity to water distribution and irrigation available water supply as constraints ,to establish channel system of work ,mul‐tiobjective optimization model .Qingling River irrigation in Yunnan ,for example ,genetic algorithm and particle swarm optimization algorithm optimization are used to solve problem .The results show that ,genetic algorithm and particle swarm optimization can solve canal system of work optimization problems .Floodgate for each channel of switching times is optimized and can meet the needs of ir‐rigation district ,thereby reducing canal system seepage losses ,and saving water consumption for irrigation purposes .Compared to the two algorithms ,PSO algorithm computing faster ,optimize floodgate operation scheduling more reasonable .

关 键 词：高原灌区 渠系工作制度 遗传算法 粒子群算法 多目标 

分 类 号：S274.2[农业科学—农业水土工程]