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| 基于NSGA2的水库多目标优化 | |
| 引用本文： | 贠汝安,董增川,王好芳.基于NSGA2的水库多目标优化[J].山东大学学报(工学版),2010,40(6):124-128. |
| 作者姓名： | 贠汝安 董增川 王好芳 |
| 作者单位： | 1. 河海大学水文水资源与水利工程科学国家重点实验室, 江苏 南京 210098; 2. 山东大学土建与水利学院,山东 济南 250061 |
| 摘 要： | 讨论了非支配排序遗传算法(non-dominated sorting gentic algorithmⅡ,NSGA2)及其参数确定问题,利用NS-GA2对两目标水库优化调度问题进行求解,求出了问题的Pareto前端,比较了参数不同取值的优化结果。实例分析结果表明:NSGA2中遗传操作参数(包括锦标赛选择参数、模拟二进制交叉分布参数、多项式变异分布参数)对优化结果影响很小,算法具有鲁棒性,对大部分水库多目标优化问题可采用推荐值;当NSGA2种群规模和进化代数两个参数足够大时,即可得到足够多且分布均匀的Pareto前端,算法具有简便性;利用NSGA2求解水库多目标优化问题,可得到足够多且分布均匀的Pareto前端,随着种群规模和进化代数的调整,Pareto前端逐步改进,算法稳定性好,适合求解水库多目标优化调度问题。 |
| 关 键 词： | 水库 多目标 优化调度 NSGA2 Pareto前端 |
| 收稿时间： | 2010-04-23 |
Multiobjective optimization of a reservoir based on NSGA2 |
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| YUN Ru-an,DONG Zeng-chuan,WANG Hao-fang.Multiobjective optimization of a reservoir based on NSGA2[J].Journal of Shandong University of Technology,2010,40(6):124-128. | |
| Authors: | YUN Ru-an DONG Zeng-chuan WANG Hao-fang |
| Affiliation: | 1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; 2. School of Civil Engineering, Shandong University, Jinan 250061, China |
| Abstract: | The algorithm of non-dominated sorting genetic algorithm II (NSGA2)and its parameter problems were discussed and applied for multiobjective optimization of a reservoir. The Pareto front of the optimization problem was obtained and the effects of the corresponding parameters on the optimal result were discussed. The study showed that the parameters in genetic algorithms in NSGA2 (include size of tournament selection, distribution parameter in simulated binary crossover, and distribution parameter in polynomial mutation) have little effects on the optimal result, and this means that NSGA2 is robust and a set of proposed values of the parameters can be used for most multiobjective optimization problems of a reservoir. A big enough and even enough distributed Pareto front can be obtained when the two parameters,population size and generation in NSGA2 are big enough, and this means that NSGA2 is simple for its parameter regulation. The Pareto front obtained gradually improves with the regulation of population size and generation, and this means that NSGA2 is stable for multi-objective optimization problems of a reservoir. |
| Keywords: | reservoir multiobjective programming optimal operation non-dominated sorting gentic algorithm Ⅱ Pareto front |
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