基于遗传算法优化粒子群算法的支斗两级渠系优化配水研究  

Research on Optimal Water Distribution in Branch and Lateral Canals Based on Genetic Algorithm Optimized Particle Swarm Algorithm

作　　者：高建 张运鑫[1,2] GAO Jian;ZHANG Yun-xin(School of Water Conservancy and Hydropower,Hebei University of Engineering,Handan 056038,Hebei Province,China;Research Center for High-efficiency Utilization of Water Resources of Hebei Province,Handan 056038,HebeiProvince,China)

机构地区：[1]河北工程大学水利水电学院,河北邯郸056038 [2]河北省水资源高效利用工程技术研究中心,河北邯郸056038

出　　处：《节水灌溉》2023年第10期108-113,123,共7页Water Saving Irrigation

摘　　要：灌区支斗两级渠道是灌区渠系配水由续灌转为轮灌的关键衔接部分,对实现灌区渠系优化配水和提高渠系水利用系数方面具有重要作用。建立了灌区支渠和斗渠两级渠道优化配水0-1规划模型,在分析利用离散二进制粒子群算法(BPSO)和遗传算法(GA)的优缺点基础上,研究提出了混合二进制粒子群算法(GA-BPSO),应用MATLAB对BPSO算法和GA-BPSO算法进行编程计算,并通过应用案例进行检验分析。研究结果表明,GA-BPSO算法比BPSO算法效率更高,其中GA-BPSO算法在迭代大约12代左右时可得到案例的最优解,而BPSO算法则在21代左右得到最优解。GA-BPSO算法在支斗两级渠系优化配水中具有快速收敛性,该算法还有进一步优化提升的空间。This paper establishes a 0-1 linear programming model for optimal water distribution in branch and lateral canals in irrigation districts,and analyzes the advantages and disadvantages of using discrete binary particle swarm algorithm(BPSO)and genetic algorithm(GA)to optimize water distribution in irrigation districts.The hybrid binary particle swarm algorithm(GA-BPSO)is proposed based on the analysis of the advantages and disadvantages of discrete binary particle swarm algorithm(BPSO)and genetic algorithm(GA).MATLAB is applied to solve the BPSO algorithm and GA-BPSO algorithm,and the application cases is used to examine and analyze the above method.The results show that GA-BPSO algorithm is more efficiently than BPSO algorithm is searching the optimal solution.For the study case,the GA-BPSO algorithm can obtain the optimal solution in about 12 iterations while the BPSO algorithm obtains the optimal solution in about 21 iterations.The GA-BPSO algorithm has fast convergence in optimizing the branch and lateral two-stage canal system water distribution,and the method still has room for further improvement.

关 键 词：渠系配水 轮灌分组 优化配水 遗传算法 混合二进制粒子群算法 

分 类 号：S275[农业科学—农业水土工程]