自适应差分进化算法在灌区频率分析中的应用
|
摘要
在较短水文系列下灌区规划设计水文频率分析中,枯水设计年的年径流量和年降雨量的精确度难以保证。差分进化算法具有收敛快、参数较少、较强的鲁棒性等特性,但标准差分进化算法具有搜索能力与开发能力相矛盾的缺点。【目的】引入一种随机扰动与三角函数相结合的迭代结构,保证迭代过程中变异随机性的同时加大收敛速率,构建可用于有较短水文系列的P-Ⅲ型分布曲线参数估值的自适应差分进化算法。【方法】以江西省赣州市南康区窑下坝站的实测降雨资料为例,分别选取1956—2010年和1981—2010年实测降雨量资料为长短系列降雨资料,计算长系列降雨资料下应用OLS准则的离差平方和及其在不同迭代次数下的标准差,计算长短系列降雨资料下不同枯水年的设计值,将自适应差分进化算法与遗传算法及传统的参数估计方法加以比较。【结果】自适应差分进化算法效率高、精度高、寻优结果稳定、鲁棒性强,在长短系列之间4个枯水年的设计值的误差之和最小。【结论】自适应差分进化算法适用于灌区规划的水文频率分析。
【Objective】Hydrological frequency analysis in planning and designing irrigation schedule for irrigation districts needs to dealt with short history of hydrological data, making the estimated annual runoff and rainfall in the design for dry years susceptible to uncertainty. The differential evolution method is simple and converges faster; it is also robust and needs less parameters, but with a conflict in search ability and potential for further development.【Method】This paper presents an iterative method by combining stochastic perturbation and trigonometric functions to ensure that the variation during the iteration was random in order to increase convergence. We constructed adaptive differential evolution algorithms in which a parameter estimation method can be used for the P-III distribution curve with short hydrology series. Taking the measured rainfall data from Yaoxiaba Station in Nankang District, Ganzhou City, Jiangxi Province as examples, rainfall measured in 55 years and 30 years were treated as long and short series respectively. Using the adaptive differential evolution algorithm, the genetic algorithm and the traditional parameter estimation method, the sum of squared deviation of the OLS and its standard deviation at different iterations for the long series of rainfall data for different dry years under the long and the short series were calculated and compared.【Result】The adaptive differential evolution algorithm was more efficient, accurate, robust, giving stable search results. For the long and short series, the adaptive differential evolution algorithm minimizes the sum of the errors in design for a period of four dry years.【Conclusion】The adaptive differential evolution algorithm is suitable for irrigation planning.
【Objective】Hydrological frequency analysis in planning and designing irrigation schedule for irrigation districts needs to dealt with short history of hydrological data, making the estimated annual runoff and rainfall in the design for dry years susceptible to uncertainty. The differential evolution method is simple and converges faster; it is also robust and needs less parameters, but with a conflict in search ability and potential for further development.【Method】This paper presents an iterative method by combining stochastic perturbation and trigonometric functions to ensure that the variation during the iteration was random in order to increase convergence. We constructed adaptive differential evolution algorithms in which a parameter estimation method can be used for the P-III distribution curve with short hydrology series. Taking the measured rainfall data from Yaoxiaba Station in Nankang District, Ganzhou City, Jiangxi Province as examples, rainfall measured in 55 years and 30 years were treated as long and short series respectively. Using the adaptive differential evolution algorithm, the genetic algorithm and the traditional parameter estimation method, the sum of squared deviation of the OLS and its standard deviation at different iterations for the long series of rainfall data for different dry years under the long and the short series were calculated and compared.【Result】The adaptive differential evolution algorithm was more efficient, accurate, robust, giving stable search results. For the long and short series, the adaptive differential evolution algorithm minimizes the sum of the errors in design for a period of four dry years.【Conclusion】The adaptive differential evolution algorithm is suitable for irrigation planning.
引文
[1]郭倩,刘攀,徐小伟,等.以平均设计流量为指标的汛期分期方法研究[J].中国农村水利水电, 2011(8):53-55.
[2]王占海,陈元芳,倪夏梅,等.遗传算法在P-Ⅲ型分布曲线参数估值中的应用[J].人民黄河, 2009, 31(9):21-23.
[3]宋茂斌,冯宝平,张展羽.基于遗传算法的皮尔逊Ⅲ型曲线参数估计[J].中国农村水利水电,2008(6):52-54.
[4]郭铭.基于遗传算法的滴灌支管轮灌小区管网优化布置研究[J].灌溉排水学报,2018,37(2):72-76.
[5]张宇亮,张礼兵,周玉良,等.基于改进加速遗传算法的作物灌水率图修正研究[J].灌溉排水学报,2015,34(11):80-83.
[6]刘力,周建中,杨俊杰,等.粒子群优化适线法在水文频率分析中的应用[J].水文, 2009, 29(2):21-23.
[7]肖琳,邱林,陈晓楠.基于粒子群算法的设计洪水过程线推求优化方法[J].水电能源科学, 2008, 26(1):56-59.
[8]姚德仓,宋松柏.设计洪水频率曲线的粒子群优化适线法研究[J].水土保持通报, 2007, 27(6):113-115.
[9]张伟,何武全.基于粒子群算法的抛物线形渠道断面优化方法[J].灌溉排水学报,2017,36(4):94-98.
[10]陈子全,宋松柏,曾智,等.基于模拟退火算法的皮尔逊Ⅲ型分布参数估计[J].人民黄河, 2012, 34(5):14-15.
[11]李宏伟,宋松柏.蚁群算法在水文频率曲线参数计算中的应用[J].人民黄河, 2009, 31(4):38-40.
[12]曹雪芹,陈元芳,陈琼,等.蚁群算法在P-Ⅲ型分布曲线参数估计中的应用[J].水电能源科学, 2010, 28(4):14-15,74.
[13]陈南祥,甘甜,杜青辉. SCEM-UA优化算法在水文频率分析中的应用[J].西北农林科技大学学报(自然科学版), 2011(8):210-214.
[14]吴光琼.群居蜘蛛优化算法在水文频率曲线参数优化中的应用[J].水资源与水工程学报, 2015, 26(6):123-126,131.
[15]王文川,雷冠军,刘惠敏,等.基于群居蜘蛛优化算法的自适应数值积分皮尔逊-Ⅲ型曲线参数估计[J].应用基础与工程科学学报, 2015, 23(S1):122-133.
[16] DAS S, MULLICK S S, SUGANTHAN P N. Recent advances in differential evolution–An updated survey[J]. Swarm&Evolutionary Computation,2016, 27:1-30.
[17] NERI F, TIRRONEN V. Recent advances in differential evolution:a survey and experimental analysis[J]. Artificial Intelligence Review, 2010, 33(1/2):61-106.
[18]汪慎文,丁立新,张文生,等.差分进化算法研究进展[J].武汉大学学报(理学版),2014,60(4):283-292.
[19]丁青锋,尹晓宇.差分进化算法综述[J].智能系统学报, 2017, 12(4):431-442.
[20]廖雄鹰,李俊,罗阳坤,等.基于自适应变异算子的差分进化算法[J].计算机工程与应用,2018,54(6):128-134,142.
[21] STORN R, PRICE K. Differential Evolution–A simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4):341-359.
[22]刘昊,丁进良,杨翠娥,等.基于择优学习策略的差分进化算法[J].上海交通大学学报, 2017, 51(6):704-708.
[23]王媛媛,刘东海.基于自适应差分进化算法的堆石坝参数反演[J].水电能源科学, 2017(6):62-66.
[24]李荣雨,陈庆倩,陈菲尔.改进种群多样性的双变异差分进化算法[J].运筹学学报, 2017, 21(1):44-54.
[25]徐劭璇.差分进化算法在交流电机矢量控制系统中的仿真研究[D].上海:东华大学, 2013.
[26]张旺.基于差分进化算法的反射面天线赋形技术研究[D].西安:西安电子科技大学, 2014.
[27]印兴耀,孔栓栓,张繁昌,等.基于差分进化算法的叠前AVO反演[J].石油地球物理勘探, 2013, 48(4):591-596.
[28] RONKKONEN J, KUKKONEN S, PRICE K V. Real-parameter optimization with differential evolution[C]//Proc. 2005 IEEE Congress on Evolutionary Computation, 2005,1:506-513.
[29]王文川,雷冠军,刘灿灿,等.群居蜘蛛优化算法在水文频率分析中的应用[J].水文, 2016, 36(3):34-39.
[30]张锦华,宋来锁,张元华,等.加权变异策略动态差分进化算法[J].计算机工程与应用, 2017,53(4):156-162.
[31] YI W, ZHOU Y, LIANG G, et al. An improved adaptive differential evolution algorithm for continuous optimization[M]. Pergamon Press, Inc. 2016.
[32]艾兵,董明刚,敬超.基于多策略排序变异的多目标差分进化算法[J].计算机应用研究, 2018, 35(7):1 950-1 954.
[33]王鑫,王人福,蒋华.改进贪婪算法的云任务调度研究[J].微电子学与计算机, 2018, 35(2):109-112,117.
[34]刘鹏.云计算[M].北京:电子工业出版社, 2015.
[2]王占海,陈元芳,倪夏梅,等.遗传算法在P-Ⅲ型分布曲线参数估值中的应用[J].人民黄河, 2009, 31(9):21-23.
[3]宋茂斌,冯宝平,张展羽.基于遗传算法的皮尔逊Ⅲ型曲线参数估计[J].中国农村水利水电,2008(6):52-54.
[4]郭铭.基于遗传算法的滴灌支管轮灌小区管网优化布置研究[J].灌溉排水学报,2018,37(2):72-76.
[5]张宇亮,张礼兵,周玉良,等.基于改进加速遗传算法的作物灌水率图修正研究[J].灌溉排水学报,2015,34(11):80-83.
[6]刘力,周建中,杨俊杰,等.粒子群优化适线法在水文频率分析中的应用[J].水文, 2009, 29(2):21-23.
[7]肖琳,邱林,陈晓楠.基于粒子群算法的设计洪水过程线推求优化方法[J].水电能源科学, 2008, 26(1):56-59.
[8]姚德仓,宋松柏.设计洪水频率曲线的粒子群优化适线法研究[J].水土保持通报, 2007, 27(6):113-115.
[9]张伟,何武全.基于粒子群算法的抛物线形渠道断面优化方法[J].灌溉排水学报,2017,36(4):94-98.
[10]陈子全,宋松柏,曾智,等.基于模拟退火算法的皮尔逊Ⅲ型分布参数估计[J].人民黄河, 2012, 34(5):14-15.
[11]李宏伟,宋松柏.蚁群算法在水文频率曲线参数计算中的应用[J].人民黄河, 2009, 31(4):38-40.
[12]曹雪芹,陈元芳,陈琼,等.蚁群算法在P-Ⅲ型分布曲线参数估计中的应用[J].水电能源科学, 2010, 28(4):14-15,74.
[13]陈南祥,甘甜,杜青辉. SCEM-UA优化算法在水文频率分析中的应用[J].西北农林科技大学学报(自然科学版), 2011(8):210-214.
[14]吴光琼.群居蜘蛛优化算法在水文频率曲线参数优化中的应用[J].水资源与水工程学报, 2015, 26(6):123-126,131.
[15]王文川,雷冠军,刘惠敏,等.基于群居蜘蛛优化算法的自适应数值积分皮尔逊-Ⅲ型曲线参数估计[J].应用基础与工程科学学报, 2015, 23(S1):122-133.
[16] DAS S, MULLICK S S, SUGANTHAN P N. Recent advances in differential evolution–An updated survey[J]. Swarm&Evolutionary Computation,2016, 27:1-30.
[17] NERI F, TIRRONEN V. Recent advances in differential evolution:a survey and experimental analysis[J]. Artificial Intelligence Review, 2010, 33(1/2):61-106.
[18]汪慎文,丁立新,张文生,等.差分进化算法研究进展[J].武汉大学学报(理学版),2014,60(4):283-292.
[19]丁青锋,尹晓宇.差分进化算法综述[J].智能系统学报, 2017, 12(4):431-442.
[20]廖雄鹰,李俊,罗阳坤,等.基于自适应变异算子的差分进化算法[J].计算机工程与应用,2018,54(6):128-134,142.
[21] STORN R, PRICE K. Differential Evolution–A simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4):341-359.
[22]刘昊,丁进良,杨翠娥,等.基于择优学习策略的差分进化算法[J].上海交通大学学报, 2017, 51(6):704-708.
[23]王媛媛,刘东海.基于自适应差分进化算法的堆石坝参数反演[J].水电能源科学, 2017(6):62-66.
[24]李荣雨,陈庆倩,陈菲尔.改进种群多样性的双变异差分进化算法[J].运筹学学报, 2017, 21(1):44-54.
[25]徐劭璇.差分进化算法在交流电机矢量控制系统中的仿真研究[D].上海:东华大学, 2013.
[26]张旺.基于差分进化算法的反射面天线赋形技术研究[D].西安:西安电子科技大学, 2014.
[27]印兴耀,孔栓栓,张繁昌,等.基于差分进化算法的叠前AVO反演[J].石油地球物理勘探, 2013, 48(4):591-596.
[28] RONKKONEN J, KUKKONEN S, PRICE K V. Real-parameter optimization with differential evolution[C]//Proc. 2005 IEEE Congress on Evolutionary Computation, 2005,1:506-513.
[29]王文川,雷冠军,刘灿灿,等.群居蜘蛛优化算法在水文频率分析中的应用[J].水文, 2016, 36(3):34-39.
[30]张锦华,宋来锁,张元华,等.加权变异策略动态差分进化算法[J].计算机工程与应用, 2017,53(4):156-162.
[31] YI W, ZHOU Y, LIANG G, et al. An improved adaptive differential evolution algorithm for continuous optimization[M]. Pergamon Press, Inc. 2016.
[32]艾兵,董明刚,敬超.基于多策略排序变异的多目标差分进化算法[J].计算机应用研究, 2018, 35(7):1 950-1 954.
[33]王鑫,王人福,蒋华.改进贪婪算法的云任务调度研究[J].微电子学与计算机, 2018, 35(2):109-112,117.
[34]刘鹏.云计算[M].北京:电子工业出版社, 2015.