基于人工鱼群算法的水库优化调度研究
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摘要
针对当前各种启发式算法,如遗传算法(GA),粒子群算法(PSO),模拟退火算法(SA)等在求解水库优化调度中的不足,提出了将新型的集群智能算法-人工鱼群算法AFSA(Artificial Fish School Algorithm)用于求解水库优化调度问题。该算法通过模拟鱼群的一些基本行为,如捕食、聚群、追尾,来求解问题的最优解。根据水库优化调度问题的情况及数学模型,给出了基于人工鱼群算法的水库优化调度的求解策略,详细讨论了求解步骤,最后给出了实验仿真结果。结果表明该算法具有较强的局部搜索能力,同时也有更高的搜索效率,与其它方法相比,该算法能够找到更优解,验证了该算法的可行性和有效性。
To reservoir operation optimization problem,people tried to find the precise method for result with many heuristic algorithms,such as Genetic algorithm(GA),Simulated Annealing algorithm(SA),Particle Swarm Optimization(PSO),etc.But these algorithms have some flaws at the moment.This paper puts forward Artificial Fish Swarm Algorithm(AFSA) for Reservoir operation optimization.The algorithm searches best result through simulating fish’s basic action,such as prey,swarm and follow.To the problem of reservoir operation optimization,it discusses the solving strategy based on AFSA.According to the mathematic model,the detailed steps are put forward.At last,by calculations of the example and comparison with other algorithms,it proves the algorithm has much stronger ability of local search and better search efficiency.It also can find better solution.It certifies that this method is feasible and valid.
To reservoir operation optimization problem,people tried to find the precise method for result with many heuristic algorithms,such as Genetic algorithm(GA),Simulated Annealing algorithm(SA),Particle Swarm Optimization(PSO),etc.But these algorithms have some flaws at the moment.This paper puts forward Artificial Fish Swarm Algorithm(AFSA) for Reservoir operation optimization.The algorithm searches best result through simulating fish’s basic action,such as prey,swarm and follow.To the problem of reservoir operation optimization,it discusses the solving strategy based on AFSA.According to the mathematic model,the detailed steps are put forward.At last,by calculations of the example and comparison with other algorithms,it proves the algorithm has much stronger ability of local search and better search efficiency.It also can find better solution.It certifies that this method is feasible and valid.
引文
[1]罗云霞,周慕逊,王万良.基于遗传模拟退火算法的水库优化调度[J].华北水利水电学院学报,2004,25(3):20-22.LUO Yun-xia,ZHOU Mu-xun,WANG Wan-liang.Single Reservoir Optimal Operation Based on Genetic Algorithms and Simulated Annealing[J].Journal of North China of Water Conservancy and Hydroelectric Power,2004,25(3):20-22.
[2]王万良,周慕逊,管秋,等.基于遗传算法的小水电站优化调度方法的研究与实践[J].水利发电学报,2005,24(3):6-10.WANG Wan-liang,ZHOU Mu-xun,GUAN Qiu,et al.Research and Practice of Optimum Operation Method Based on Genetic Algorithm for Small Hydropower Stations[J].Journal of Hydroelectric Engineering,2005,24(3):6-10.
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[7]李崇浩,纪昌明,李文武.改进微粒群算法及其在水库优化调度中的应用[J].中国农村水利水电,2005,(2):54-56.LI Chong-hao,JI Chang-ming,LI Wen-wu.Modified Particle Swarm Algorithm and Its Application in Reservoir Operation Optimization[J].China Rural Water and Hydropower,2005,(2):54-56.
[8]李晓磊.一种新型的智能优化方法——人工鱼群算法[D].杭州:浙江大学,2003.LI Xiao-lei.A New Intelligent Optimization Method–Artificial Fish School Algorithm[D].Hangzhou:Zhejang University,2003.
[9]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,(11):32-38.LI Xiao-lei,SHAO Zhi-jiang,QIAN Ji-xin.An Optimizing Method Based on Autonomous Animats:Fish-swarm Algorithm[J].Systems Engineering-theory&Practice,2002,(11):32-38.
[10]李晓磊,钱积新.基于分解协调的人工鱼群优化算法研究[J].电路与系统学报,2003,8(1):1-6.LI Xiao-lei,QIAN Ji-xin.Studies on Artificial Fish Swarm Optimization Algorithm based on Decomposition and Coordination Techniques[J].Journal of Circuits and Systems,2003,8(1):1-6.
[2]王万良,周慕逊,管秋,等.基于遗传算法的小水电站优化调度方法的研究与实践[J].水利发电学报,2005,24(3):6-10.WANG Wan-liang,ZHOU Mu-xun,GUAN Qiu,et al.Research and Practice of Optimum Operation Method Based on Genetic Algorithm for Small Hydropower Stations[J].Journal of Hydroelectric Engineering,2005,24(3):6-10.
[3]周培德.算法设计与分析[M].北京:机械工业出版社,1996.ZHOU Pei-de.Design and Analysis of Algorithms[M].Beijing:China Machine Press,1996.
[4]武鹏林,霍德敏.水利计算与水库调度[M].北京:地震出版社,2000.WU Peng-lin,HUO De-min.Water Conservancy Calculation and Reservoir Operation[M].Beijing:Earthquake Publishing Press,2000.
[5]徐鼎甲,张玉山.混联水电站群实时联合优化调度[J].水力发电学报,2001,(3):68-74.XU Ding-jia,ZHANG Yu-shan.Real-time Uniting-optimum Operation for the Large Scale Hydroelectric Power System[J].Journal of Hydroelectric Engineering,2001,3:68-74.
[6]马广文,王黎.遗传算法在水电站优化调度中的应用[J].水科学进展,1997,18(3):275-280.MA Guang-wen,WANG Li.Application of a Genetic Algorithm to Optimal Operation of Hydropower Station[J].Advances in Water Science,1997,18(3):275-280.
[7]李崇浩,纪昌明,李文武.改进微粒群算法及其在水库优化调度中的应用[J].中国农村水利水电,2005,(2):54-56.LI Chong-hao,JI Chang-ming,LI Wen-wu.Modified Particle Swarm Algorithm and Its Application in Reservoir Operation Optimization[J].China Rural Water and Hydropower,2005,(2):54-56.
[8]李晓磊.一种新型的智能优化方法——人工鱼群算法[D].杭州:浙江大学,2003.LI Xiao-lei.A New Intelligent Optimization Method–Artificial Fish School Algorithm[D].Hangzhou:Zhejang University,2003.
[9]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,(11):32-38.LI Xiao-lei,SHAO Zhi-jiang,QIAN Ji-xin.An Optimizing Method Based on Autonomous Animats:Fish-swarm Algorithm[J].Systems Engineering-theory&Practice,2002,(11):32-38.
[10]李晓磊,钱积新.基于分解协调的人工鱼群优化算法研究[J].电路与系统学报,2003,8(1):1-6.LI Xiao-lei,QIAN Ji-xin.Studies on Artificial Fish Swarm Optimization Algorithm based on Decomposition and Coordination Techniques[J].Journal of Circuits and Systems,2003,8(1):1-6.
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