基于混合量子粒子群算法的梯级水电站群调度
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摘要
针对标准量子粒子群(QPSO)算法在求解复杂梯级水电站群联合调度问题时存在的早熟收敛、停滞寻优等不足,提出了一种耦合两重改进策略优势的混合量子粒子群(HQPSO)算法:首先对个体极值按照一定的概率进行变异搜索操作,以增加个体多样性、强化种群全局开采能力;而后建立外部档案集合来存储进化过程中的部分精英个体,利用基于动态概率辨识机制的单纯形算子指导外部档案集中的个体开展邻域寻优,以提高算法搜索能力、避免陷入局部最优。乌江流域实践结果表明:HQPSO算法的收敛速度与全局搜索能力得到增强,有效克服了QPSO的缺陷与不足,具有一定的工程实际应用价值。
In solving the joint scheduling problem of cascade hydropower stations, the standard quantumbehaved particle swarm optimization(QPSO) algorithm suffers from premature convergence and local trapping, among other shortcomings. This paper presents a hybrid QPSO(HQPSO) that combines the advantages of the two-fold improvement strategy. This new method first does mutation search for individual extremes at a given probability to increase the diversity of individuals and enhance the global exploiting capability of the population. Then, it establishes an external archive set to conserve certain particles found in the evolutionary process. Finally, it uses the Nelder-Mead operator for dynamic probability identification to help particles searching in the neighborhood, improving its searching capability and avoiding falling into a local optimum. Application to the Wu River shows that the HQPSO is faster in convergence and global searching and practically applicable, avoiding the shortcomings of QPSO.
In solving the joint scheduling problem of cascade hydropower stations, the standard quantumbehaved particle swarm optimization(QPSO) algorithm suffers from premature convergence and local trapping, among other shortcomings. This paper presents a hybrid QPSO(HQPSO) that combines the advantages of the two-fold improvement strategy. This new method first does mutation search for individual extremes at a given probability to increase the diversity of individuals and enhance the global exploiting capability of the population. Then, it establishes an external archive set to conserve certain particles found in the evolutionary process. Finally, it uses the Nelder-Mead operator for dynamic probability identification to help particles searching in the neighborhood, improving its searching capability and avoiding falling into a local optimum. Application to the Wu River shows that the HQPSO is faster in convergence and global searching and practically applicable, avoiding the shortcomings of QPSO.
引文
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[10]王森,程春田,武新宇,等.自适应混沌整体退火遗传算法在水电站群优化调度中的应用[J].水力发电学报,2014,33(5):63-71.WANG Sen,CHENG Chuntian,WU Xinyu,et al.Application of self-adaptive chaos whole annealing genetic algorithm to optimal operation of hydropower station groups[J].Journal of Hydroelectric Engineering,2014,33(5):63-71.(in Chinese)
[11]纪昌明,李继伟,张新明.梯级水电站短期发电优化调度的免疫蛙跳算法应用研究[J].水力发电学报,2015,34(1):29-36.JI Changming,LI Jiwei,ZHANG Xinming.Application of immune-shuffled frog-leaping algorithm to optimized operation of cascade hydropower stations for short-term power generation[J].Journal of Hydroelectric Engineering,2015,34(1):29-36.(in Chinese)
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[14]万芳,邱林,黄强.水库群供水优化调度的免疫蚁群算法应用研究[J].水力发电学报,2011,30(5):234-239.WAN Fang,QIU Lin,HUANG Qiang.Application of ant colony operation based on immune evolutionary algorithm to optimal reservoir water supply dispatching[J].Journal of Hydroelectric Engineering,2011,30(5):234-239.(in Chinese)
[15]王文川,雷冠军,邱林,等.群居蜘蛛优化算法在水电站优化调度中的应用及其效能分析[J].水力发电学报,2015,34(10):80-87.WANG Wenchuan,LEI Guanjun,QIU Lin,et al.Optimal operation of hydropower stations using social spider optimization algorithm and its performance analysis[J].Journal of Hydroelectric Engineering,2015,34(10):80-87.(in Chinese)
[16]Sun J,Fang W,Wang D,et al.Solving the economic dispatch problem with a modified quantum-behaved particle swarm optimization method[J].Energy Conversion and Management,2009,50(12):2967-2975.
[17]王道军,孙俊,须文波.量子粒子群算法在电力系统经济调度中的应用[J].计算机工程与设计,2008,29(19):5019-5021.WANG Daojun,SUN Jun,XU Wenbo.Quantumbehaved particle swarm optimization for economic dispatch[J].Computer Engineering and Design,2008,29(19):5019-5021.(in Chinese)
[18]王兰彬.量子粒子群优化算法在结构参数识别中的应用[J].防灾减灾工程学报,2013,33(1):91-96.WANG Lanbin.Application of quantum particle swarm optimization algorithm in structural parameter estimation[J].Journal of Disaster Prevention and Mitigation Engineering,2013,33(1):91-96.(in Chinese)
[19]范秋枫,王涛,张智峰.量子粒子群智能算法在国际布伦特原油价格预测中的应用[J].模糊系统与数学,2017,31(4):84-90.FAN Qiufeng,WANG Tao,ZHANG Zhifeng.Quantum particle swarm intelligence algorithm in the international Brent crude oil price forecast[J].Fuzzy Systems and Mathematics,2017,31(4):84-90.(in Chinese)
[20]Xi M L,Qi Z M,Zou Y,et al.Calibrating RZWQM2model using quantum-behaved particle swarm optimization algorithm[J].Computers and Electronics in Agriculture,2015,113:72-80.
[21]Ch S,Anand N,Panigrahi B K,et al.Streamflow forecasting by SVM with quantum behaved particle swarm optimization[J].Neurocomputing,2013,101:18-23.
[22]Singh M R,Mahapatra S S.A quantum behaved particle swarm optimization for flexible job shop scheduling[J].Computers&Industrial Engineering,2016,93:36-44.
[23]黄宇,韩璞,刘长良,等.改进量子粒子群算法及其在系统辨识中的应用[J].中国电机工程学报,2011,31(20):114-120.HUANG Yu,HAN Pu,LIU Changliang,et al.Application of improved quantum particle swarm optimization in system identification[J].Proceedings of the CSEE,2011,31(20):114-120.(in Chinese)
[24]Haddar B,Khemakhem M,Wilbaut C.A hybrid quantum particle swarm optimization for the Multidimensional Knapsack problem[J].Engineering Applications of Artificial Intelligence,2016,55:1-13.
[25]Hassani K,Lee W S.Multi-objective design of state feedback controllers using reinforced quantum-behaved particle swarm optimization[J].Applied Soft Computing,2016,41:66-76.
[26]冯仲恺,廖胜利,牛文静,等.改进量子粒子群算法在水电站群优化调度中的应用[J].水科学进展,2015,26(3):413-422.FENG Zhongkai,LIAO Shengli,NIU Wenjing,et al.Application of improved quantum particle swarm optimization algorithm in hydropower station group[J].Advances in Water Science,2015,26(3):413-422.(in Chinese)
[27]邹强,王学敏,李安强,等.基于并行混沌量子粒子群算法的梯级水库群防洪优化调度研究[J].水利学报,2016,47(8):967-976.ZOU Qiang,WANG Xuemin,LI Anqiang,et al.Optimal operation of flood control for cascade reservoirs based on parallel chaotic quantum particle swarm optimization[J].Journal of Hydraulic Engineering,2016,47(8):967-976.(in Chinese)
[28]郑慧涛,梅亚东,胡挺,等.双层交互混合差分进化算法在水库群优化调度中的应用[J].水力发电学报,2013,32(1):54-62.ZHENG Huitao,MEI Yadong,HU Ting,et al.Doubletier interaction shuffled differential evolution algorithm and its application to optimal operation of cascade reservoirs[J].Journal of Hydroelectric Engineering,2013,32(1):54-62.(in Chinese)
[29]牛文静,冯仲恺,程春田.梯级水电站群优化调度多目标量子粒子群算法[J].水力发电学报,2017,36(5):47-57.NIU Wenjing,FENG Zhongkai,CHEN Chuntian.Multiobjective quantum particle swarm optimization of operation cascade hydropower stations[J].Journal of Hydroelectric Engineering,2017,36(5):47-57.(in Chinese)
[2]黄强,赵梦龙,李瑛.水库生态调度研究新进展[J].水力发电学报,2017,36(3):1-11.HUANG Qiang,ZHAO Menglong,LI Ying.Advancements in studies on reservoir ecological operation[J].Journal of Hydroelectric Engineering,2017,36(3):1-11.(in Chinese)
[3]Ming B,Chang J X,Huang Q,et al.Optimal operation of multi-reservoir system based-on cuckoo search algorithm[J].Water Resources Management,2015,29:5671-5687.
[4]郭生练,陈炯宏,刘攀,等.水库群联合优化调度研究进展与展望[J].水科学进展,2010,21(4):496-503.GUO Shenglian,CHEN Jionghong,LIU Pan,et al.Stateof-the-art review of joint operation for multi-reservoir systems[J].Advances in Water Science,2010,21(4):496-503.(in Chinese)
[5]万新宇,王光谦.基于并行动态规划的水库发电优化[J].水力发电学报,2011,30(6):166-170.WAN Xinyu,WANG Guangqian.Parallel dynamic programming for hydropower generation optimization[J].Journal of Hydroelectric engineering,2011,30(6):166-170.(in Chinese)
[6]Zhao T T G,Zhao J S,Yang D W.Improved dynamic programming for hydropower reservoir operation[J].Journal of Water Resources Planning and Management,2014,140(3):360-374.
[7]周佳,马光文,张志刚.基于改进POA算法的雅砻江梯级水电站群中长期优化调度研究[J].水力发电学报,2010,29(3):18-22.ZHOU Jia,MA Guangwen,ZHANG Zhigang.Study on the mid-long term optimal dispatching of cascade hydropower stations on Yalong River based on POAmodified adaptive algorithm[J].Journal of Hydroelectric Engineering,2010,29(3):18-22.(in Chinese)
[8]张诚,周建中,王超,等.梯级水电站优化调度的变阶段逐步优化算法[J].水力发电学报,2016,35(4):12-21.ZHANG Cheng,ZHOU Jianzhong,WANG Chao,et al Variable period progressive optimality algorithm for optimal dispatch of cascade reservoirs[J].Journal of Hydroelectric Engineering,2016,35(4):12-21.(in Chinese)
[9]Li C G,Ji C M,Wang B Q,et al.The hydropower station output function and its application in reservoir operation[J].Water Resources Management,2017,31(1):159-172.
[10]王森,程春田,武新宇,等.自适应混沌整体退火遗传算法在水电站群优化调度中的应用[J].水力发电学报,2014,33(5):63-71.WANG Sen,CHENG Chuntian,WU Xinyu,et al.Application of self-adaptive chaos whole annealing genetic algorithm to optimal operation of hydropower station groups[J].Journal of Hydroelectric Engineering,2014,33(5):63-71.(in Chinese)
[11]纪昌明,李继伟,张新明.梯级水电站短期发电优化调度的免疫蛙跳算法应用研究[J].水力发电学报,2015,34(1):29-36.JI Changming,LI Jiwei,ZHANG Xinming.Application of immune-shuffled frog-leaping algorithm to optimized operation of cascade hydropower stations for short-term power generation[J].Journal of Hydroelectric Engineering,2015,34(1):29-36.(in Chinese)
[12]李文莉,李郁侠,任平安.基于云变异人工蜂群算法的梯级水库群优化调度[J].水力发电学报,2014,33(1):38-42.LI Wenli,LI Yuxia,REN Ping’an.Optimal operation of cascade reservoirs based on cloud variation-artificial bee colony algorithm[J].Journal of Hydroelectric Engineering,2014,33(1):38-42.(in Chinese)
[13]王森,武新宇,程春田,等.自适应混合粒子群算法在梯级水电站群优化调度中的应用[J].水力发电学报,2012,31(1):38-44.WANG Sen,WU Xinyu,CHENG Chuntian,et al.Application of self-adaptive hybrid particle swarm optimization algorithm to optimal operation of cascade reservoirs[J].Journal of Hydroelectric Engineering,2012,31(1):38-44.(in Chinese)
[14]万芳,邱林,黄强.水库群供水优化调度的免疫蚁群算法应用研究[J].水力发电学报,2011,30(5):234-239.WAN Fang,QIU Lin,HUANG Qiang.Application of ant colony operation based on immune evolutionary algorithm to optimal reservoir water supply dispatching[J].Journal of Hydroelectric Engineering,2011,30(5):234-239.(in Chinese)
[15]王文川,雷冠军,邱林,等.群居蜘蛛优化算法在水电站优化调度中的应用及其效能分析[J].水力发电学报,2015,34(10):80-87.WANG Wenchuan,LEI Guanjun,QIU Lin,et al.Optimal operation of hydropower stations using social spider optimization algorithm and its performance analysis[J].Journal of Hydroelectric Engineering,2015,34(10):80-87.(in Chinese)
[16]Sun J,Fang W,Wang D,et al.Solving the economic dispatch problem with a modified quantum-behaved particle swarm optimization method[J].Energy Conversion and Management,2009,50(12):2967-2975.
[17]王道军,孙俊,须文波.量子粒子群算法在电力系统经济调度中的应用[J].计算机工程与设计,2008,29(19):5019-5021.WANG Daojun,SUN Jun,XU Wenbo.Quantumbehaved particle swarm optimization for economic dispatch[J].Computer Engineering and Design,2008,29(19):5019-5021.(in Chinese)
[18]王兰彬.量子粒子群优化算法在结构参数识别中的应用[J].防灾减灾工程学报,2013,33(1):91-96.WANG Lanbin.Application of quantum particle swarm optimization algorithm in structural parameter estimation[J].Journal of Disaster Prevention and Mitigation Engineering,2013,33(1):91-96.(in Chinese)
[19]范秋枫,王涛,张智峰.量子粒子群智能算法在国际布伦特原油价格预测中的应用[J].模糊系统与数学,2017,31(4):84-90.FAN Qiufeng,WANG Tao,ZHANG Zhifeng.Quantum particle swarm intelligence algorithm in the international Brent crude oil price forecast[J].Fuzzy Systems and Mathematics,2017,31(4):84-90.(in Chinese)
[20]Xi M L,Qi Z M,Zou Y,et al.Calibrating RZWQM2model using quantum-behaved particle swarm optimization algorithm[J].Computers and Electronics in Agriculture,2015,113:72-80.
[21]Ch S,Anand N,Panigrahi B K,et al.Streamflow forecasting by SVM with quantum behaved particle swarm optimization[J].Neurocomputing,2013,101:18-23.
[22]Singh M R,Mahapatra S S.A quantum behaved particle swarm optimization for flexible job shop scheduling[J].Computers&Industrial Engineering,2016,93:36-44.
[23]黄宇,韩璞,刘长良,等.改进量子粒子群算法及其在系统辨识中的应用[J].中国电机工程学报,2011,31(20):114-120.HUANG Yu,HAN Pu,LIU Changliang,et al.Application of improved quantum particle swarm optimization in system identification[J].Proceedings of the CSEE,2011,31(20):114-120.(in Chinese)
[24]Haddar B,Khemakhem M,Wilbaut C.A hybrid quantum particle swarm optimization for the Multidimensional Knapsack problem[J].Engineering Applications of Artificial Intelligence,2016,55:1-13.
[25]Hassani K,Lee W S.Multi-objective design of state feedback controllers using reinforced quantum-behaved particle swarm optimization[J].Applied Soft Computing,2016,41:66-76.
[26]冯仲恺,廖胜利,牛文静,等.改进量子粒子群算法在水电站群优化调度中的应用[J].水科学进展,2015,26(3):413-422.FENG Zhongkai,LIAO Shengli,NIU Wenjing,et al.Application of improved quantum particle swarm optimization algorithm in hydropower station group[J].Advances in Water Science,2015,26(3):413-422.(in Chinese)
[27]邹强,王学敏,李安强,等.基于并行混沌量子粒子群算法的梯级水库群防洪优化调度研究[J].水利学报,2016,47(8):967-976.ZOU Qiang,WANG Xuemin,LI Anqiang,et al.Optimal operation of flood control for cascade reservoirs based on parallel chaotic quantum particle swarm optimization[J].Journal of Hydraulic Engineering,2016,47(8):967-976.(in Chinese)
[28]郑慧涛,梅亚东,胡挺,等.双层交互混合差分进化算法在水库群优化调度中的应用[J].水力发电学报,2013,32(1):54-62.ZHENG Huitao,MEI Yadong,HU Ting,et al.Doubletier interaction shuffled differential evolution algorithm and its application to optimal operation of cascade reservoirs[J].Journal of Hydroelectric Engineering,2013,32(1):54-62.(in Chinese)
[29]牛文静,冯仲恺,程春田.梯级水电站群优化调度多目标量子粒子群算法[J].水力发电学报,2017,36(5):47-57.NIU Wenjing,FENG Zhongkai,CHEN Chuntian.Multiobjective quantum particle swarm optimization of operation cascade hydropower stations[J].Journal of Hydroelectric Engineering,2017,36(5):47-57.(in Chinese)