基于遗传算法的火电机组负荷优化分配研究
|
摘要
负荷优化分配是电厂经济、安全运行的一项很重要的工作,其目的是在给定某运行时段的开停机计划后,在组合机组满足电力系统运行约束条件的基础上,合理分配各机组负荷使得发电成本、污染排放等目标尽可能小。本文针对这一优化问题对其数学模型和优化算法进行了深入的研究。
首先对电厂普遍采用的经济性指标进行分析与讨论,确定标准供电煤耗率为其优化目标,研究了基本遗传算法求解负荷优化分配问题的缺陷,并提出了相应的改进方案,在基本遗传算法的基础上提出了边界约束的初始化方案,排序选择的选择算子、自适应交叉算子以及最优值保存策略,最终收敛速度得到大幅提高。
提出了基于遗传禁忌混合算法的负荷优化分配方法,遗传算法具有较强的全局搜索能力,禁忌搜索算法具有较强的“爬山能力”,两种算法优势互补,结合后可以避免出现早熟现象。为了充分利用禁忌算法的局部搜索能力,而又防止太过频繁的调用禁忌算法造成时间复杂度大幅度提高的问题,本文提出2种算法结合的关键是在遗传算法趋向于早熟现象时通过禁忌算法跳出这种局部最优状态,利用适应度的样本方差来比较遗传算法种群的变化情况,并提出了简单可靠的早熟判断方法,在禁忌算法的设计上提出了邻域解产生的新方法。实例分析证明该算法求解大规模考虑阀点效应的负荷优化问题性能更优。
将负荷优化分配这一带约束的单目标优化问题转化为多目标优化问题来处理,建立了双目标负荷优化分配数学模型,一个目标函数为：总煤耗函数,另一个目标函数为：违反约束条件的程度函数。在评价策略、遗传算子等方面对常规的多目标遗传算法进行了改进,利用Pareto强度值作为个体的评价指标,利用遗传算法实现种群的进化,最终找到最优解,为机组负荷分配的求解提供了新的有效算法。最后通过仿真对简单遗传算法、改进遗传算法、遗传禁忌混合算法以及多目标遗传算法的性能进行了分析比较。
Load optimal distribution is a hot research issue all along in power system. On the premise of giving the unit commitment and all constraint conditions, load optimal distribution studies how to dispatch load to operating sets to makes the total operating fees lowest in power station. This paper has deeply studied optimal modes and optimal algorithms on load optimal distribution.
At first, the paper analyzes the economic indexes which are generally adopted in power station, and defines standard coal consumption as the optimal objective. The shortage of simple genetic algorithm solving economic load dispatch is researched. On the basis of the simple genetic algorithm, some improved solutions are proposed, which include increasing boundary constraint to initial population, ranking selection operator, adaptive crossover operator and optimization preservation strategy. The improved genetic algorithm is applied to three generating units, and the results shown that the improved genetic algorithm has better optimization effect.
Next, a real code genetic-tabu search hybrid algorithm is presented. Genetic algorithm is characterized by the capability of global searching, and tabu search is characterized by the capability of mountain climbing, so the advantages of two algorithms complement each other and the hybrid algorithm can avoid pre-maturity after combination. In order to fully utilize tabu search's local search ability, and also avoid using tabu search too much bringing about time complexity increasing, this paper proposes the key to combination of the two algorithms is breaking local optimum by tabu search when the genetic algorithm tends to prematurity. A simple and reliable method is advanced to estimate. prematurity, which compare the population change by sample variance. A new method is put forward to produce the neighborhood solution of tabu search. The effect of optimization is compared by case analysis, and the results demonstrate the effectiveness and viability of the algorithm.
Then, load optimal dispatch is a single objective optimization question with constraints. The question is turned into a multi-objective optimization question in this paper. One objective is the total coal consumption function, and the other is the constraint violation degree function, so that the two-objective mathematic mode is built. Some improved measures are proposed in evaluation strategy, genetic operators, etc. The evaluation function is the individual pareto strength, and the population evolution depends on genetic algorithm. This algorithm provides a new and effective method to load economic dispatch. Finally through the simulation the performance of simple genetic algorithm, improved genetic algorithm, hybrid genetic and tabu search algorithm are compared and analyzed.
首先对电厂普遍采用的经济性指标进行分析与讨论,确定标准供电煤耗率为其优化目标,研究了基本遗传算法求解负荷优化分配问题的缺陷,并提出了相应的改进方案,在基本遗传算法的基础上提出了边界约束的初始化方案,排序选择的选择算子、自适应交叉算子以及最优值保存策略,最终收敛速度得到大幅提高。
提出了基于遗传禁忌混合算法的负荷优化分配方法,遗传算法具有较强的全局搜索能力,禁忌搜索算法具有较强的“爬山能力”,两种算法优势互补,结合后可以避免出现早熟现象。为了充分利用禁忌算法的局部搜索能力,而又防止太过频繁的调用禁忌算法造成时间复杂度大幅度提高的问题,本文提出2种算法结合的关键是在遗传算法趋向于早熟现象时通过禁忌算法跳出这种局部最优状态,利用适应度的样本方差来比较遗传算法种群的变化情况,并提出了简单可靠的早熟判断方法,在禁忌算法的设计上提出了邻域解产生的新方法。实例分析证明该算法求解大规模考虑阀点效应的负荷优化问题性能更优。
将负荷优化分配这一带约束的单目标优化问题转化为多目标优化问题来处理,建立了双目标负荷优化分配数学模型,一个目标函数为：总煤耗函数,另一个目标函数为：违反约束条件的程度函数。在评价策略、遗传算子等方面对常规的多目标遗传算法进行了改进,利用Pareto强度值作为个体的评价指标,利用遗传算法实现种群的进化,最终找到最优解,为机组负荷分配的求解提供了新的有效算法。最后通过仿真对简单遗传算法、改进遗传算法、遗传禁忌混合算法以及多目标遗传算法的性能进行了分析比较。
Load optimal distribution is a hot research issue all along in power system. On the premise of giving the unit commitment and all constraint conditions, load optimal distribution studies how to dispatch load to operating sets to makes the total operating fees lowest in power station. This paper has deeply studied optimal modes and optimal algorithms on load optimal distribution.
At first, the paper analyzes the economic indexes which are generally adopted in power station, and defines standard coal consumption as the optimal objective. The shortage of simple genetic algorithm solving economic load dispatch is researched. On the basis of the simple genetic algorithm, some improved solutions are proposed, which include increasing boundary constraint to initial population, ranking selection operator, adaptive crossover operator and optimization preservation strategy. The improved genetic algorithm is applied to three generating units, and the results shown that the improved genetic algorithm has better optimization effect.
Next, a real code genetic-tabu search hybrid algorithm is presented. Genetic algorithm is characterized by the capability of global searching, and tabu search is characterized by the capability of mountain climbing, so the advantages of two algorithms complement each other and the hybrid algorithm can avoid pre-maturity after combination. In order to fully utilize tabu search's local search ability, and also avoid using tabu search too much bringing about time complexity increasing, this paper proposes the key to combination of the two algorithms is breaking local optimum by tabu search when the genetic algorithm tends to prematurity. A simple and reliable method is advanced to estimate. prematurity, which compare the population change by sample variance. A new method is put forward to produce the neighborhood solution of tabu search. The effect of optimization is compared by case analysis, and the results demonstrate the effectiveness and viability of the algorithm.
Then, load optimal dispatch is a single objective optimization question with constraints. The question is turned into a multi-objective optimization question in this paper. One objective is the total coal consumption function, and the other is the constraint violation degree function, so that the two-objective mathematic mode is built. Some improved measures are proposed in evaluation strategy, genetic operators, etc. The evaluation function is the individual pareto strength, and the population evolution depends on genetic algorithm. This algorithm provides a new and effective method to load economic dispatch. Finally through the simulation the performance of simple genetic algorithm, improved genetic algorithm, hybrid genetic and tabu search algorithm are compared and analyzed.
引文
[1]2050中国能源和碳排放研究课题组编著.2050中国能源和碳排放报告[M].2009,7.第1版,北京：科学出版社,488-500.
[2]陆延昌,21世纪初期中国电力工业展望,中国电力,2000,33(7)：1-8.
[3]王承民,于尔铿,郭志忠.电力市场中一种基于动态规划法的经济负荷分配算法[J].电力系统自动化,2000,21：89-93.
[4]Santos J R, Lora A T, Exposito A G, et al. Finding improved local minima of power system optimization problems by interior-point methods [J]. IEEE Transactions on power systems, 2003,18(1):238-244.
[5]R. A. Jabr, A. H. Coonick, B. J. Cory. A homogeneous linear programming algorithm for the security constrained economic dispatch problem [J]. IEEE Trans. Power Syst.,2000,15(3): 930-936.
[6]Daneshi C, hoobbari A.L, Shahidehpour M Mixed integer programming method to solve security constrained unit commitment with restricted operating zone limits [C]. IEEE International Conference onElectro/Information Technology,2008,5:187-192.
[7]A. G. Bakirtzis, C. E. Zoumas, Lambda of Lagrangian relaxation solution to unit commitment problem [J]. Proc. Inst. Elect. Eng. Gen.Transm. Dist.,2000,147(2):131-136.
[8]Coelho Ld.S, Mariani V.C. Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect [J]. IEEE Transactions on Power Systems,2006,21(2):989-996.
[9]刘自发,张建华.一种求解电力经济负荷分配问题的改进微分进化算法[J].中国电机工程学报,2008,28(10)：100-105.
[10]Singhal, P.K.Dynamic programming approach for solving power generating unit commitment problem [C].Computer and Communication Technology (ICCCT)2011 2nd International Conference.2011,9:298-303.
[11]Momoh JA, El-Hawary ME, Adapa R. A review of selected optimal power flow literature to 1993 part I:nonlinear and quadratic programming approaches [J]. IEEE Trans Power Syst, 1999,14(1):96-104.
[12]Momoh JA, El-Hawary ME, Adapa R. A review of selected optimal power flow literature to 1993 Part ll:Newton, linear programming and interior pointmethods [J]. IEEE Trans Power Syst,1999,14(1):105-11.
[13]Amit Kumar, Jagdeep Kaur, Pushpinder Singh.A new method for solving fully fuzzy linear programming problems [J]. Applied Mathematical Modelling.2011,35(2):817-823.
[14]Waight J.G, Bose A, Sheble G.B. Generation Dispatch with Reserve Margin Constraints Using Linear Programming [J]. IEEE Transactions on Power Apparatus and Systems.1981, 100(1):252-258.
[15]SimopoulosD.N, Kavatza S.D.Reliability Constrained Unit Commitment Using Simulated Annealing [J]. IEEE Transactions on Power Systems,2006,21(04):1699-1706.
[16]A. Viana, J. P. Sousa, and M. Matos, Simulated annealing for the unit commitment problem, the IEEE Porto Power Tech Conf [C],2001,9:10-13.
[17]Holland J H. Genetic Algorithms [J]. Scientific American,1992,267(1):44-50.
[18]Yu Tingfang, Lin Zhongda. Application of float genetic algorithms-partially solved combined with punishing function in power plant units commitment problem [J]. Proceedings of the CSEE,2009,29(2):107-110.
[19]Dang C, Li M. A floating-point genetic algorithm for solving the unit commitment problem [J]. Eur J Oper Res.2007,187:1370-1395.
[20]Chiang C L. Genetic algorithms for power economic [J]. IET on generation transmission and distribution,2007, 1(2):261-269.
[21]CHEN Yan-qiao, NI Min, LIU Ji-zhen, WEI Xiang-guo. Application of Real-code Genetic Algorithm to Economic Load Dispatch in Power Plants [J]. Proceedings of the CSEE,2007, 27(20):107-112.
[22]David C, Gerald B. Genetic algorithm solution of economic dispatch with valve point loading [J]. IEEE Transactions on Power Systems,1993,8(3):1325-1332.
[23]闫顺林,李太兴,刘振刚.遗传算法搜索优化及其在机组负荷优化分配中的应用[J].东北电力技术,2007,28(10)：20-22.
[24]JAndre, PSiarry, TDognon. An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization [J]. Advance in Engineering Software, 2001,32(1):49-60.
[25]叶正华,谢勇,郑金华.一种改进的基于实数编码的遗传算法[J].湘潭大学自然科学学报,2002,24(3)：32-35.
[26]余廷芳,林中达,林显敏.部分解约束改进遗传算法在火电厂机组负荷优化分配中的应用[J].汽轮机技术,2006,48(5)：352-355.
[27]方彦军,伍洲,王琛.集散遗传算法在厂级AGC负荷分配中的应用[J].电网技术,2010,7(34)：190-194.
[28]Wona Jong-Ryul, Park Young-Moon. Economic dispatch solutions with piecewise quadratic cost function using improved genetic algorithm [J]. Electric Power Energy Syst.2003,25 (5):355-361.
[29]倪敏,陈彦桥,刘吉臻.基于遗传算法的火电机组负荷优化分配研究[J].华北电力大学学报,2006,33(5)：65-67.
[30]谈英姿,沈炯,吕震中,免疫优化算法及其前景展望,信息与控制,2002,31(5)：385-390
[31]Cutello V, Nicosia G., Romeo M, Oliveto P.S. On the Convergence of Immune Algorithms [C]. Foundations of Computational Intelligence, FOCI 2007, IEEE Symposium.2007,4: 409-415.
[32]Na-Na Li, Yong-Feng Dong, Jun-Hua Gu, Rui-Ying Zhou. A New Algorithm Based on Immune Algorithm and Hopfield Neural Network for Multimodal Function Optimization [C]. Machine Learning and Cybernetics,2007 International Conference on.2007,8: 2837-2840.
[33]李蔚,刘长东,盛德仁,陈坚红,袁镇福,岑可法,免疫算法在火电机组优化组合中的应用,浙江大学学报(工学版),2004,38(8)：1090-1094
[34]Taher Niknam.A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem [J].Applied Energy. 2010,87(1):327-339.
[35]D. N. Jeyakumar, T. Jayabarathi, T. Raghunathan. Particle swarm optimization for various types of economic dispatch problems [J], International Journal of Electrical Power & Energy Systems,2006,28(1):36-42.
[36]SriyanyongP, SongY.H. Unit commitment using particle swarm optimization combined with Lagrange relaxation[C].2005 IEEE Power Engineering Society General Meeting,2005,6: 2752-2759.
[37]AlRashidi, M. R.,El-Hawary,M. E.. A survey of particle swarm optimization applications in electric power systems [J]. IEEE Trans. Evolution Commutate,2009,13 (4):913-918.
[38]Hou Yunhe, Lu Lijuan, Xiong Xinliang, et al. Enhanced particle swarm optimal algorithm and its application on one economic dispatch of power system [J]. Proceedings of the CSEE, 2004,24(7):95-100.
[39]J.-B. Park, K.-S. Lee, J.-R. Shin, K. Y. Lee. A particle swarm optimization for economic dispatch with nonsmooth cost functions [J], IEEE Trans. Power Syst.,2005,20(1):34-42.
[40]侯云鹤,鲁丽娟,熊信良等.改进粒子群及其在电力系统经济负荷分配中的应用[J].中国电机工程学报,2004,24(7)：95-100.
[41]修春波,陆丽芬.改进的混沌优化算法及其在电力系统经济负荷分配中的应用研究[J].电力系统保护与控制,2010,38(21)：109-112.
[42]唐巍,李殿璞.电力系统经济负荷分配的混沌优化方法[J].中国电机工程学报,2000,20(10)：36-40.
[43]廖艳芬,马晓茜.改进的混沌优化方法化电站机组负荷分配中的应用[J].动力工程,2006,26(1)：93-96.
[44]下爽心,韩芳,朱衡君.基于改进变尺度混沌优化方法的经济负荷分配[J].中国电机工程学报,2005,25(24)：90-95.
[45]J Nanda J, Mishra S, Saikia Lalit Chandra. Maiden application of bacterial foraging based optimization technique in multiarea automatic generation control [J]. IEEE Trans Power Syst,2009,24(2):602-609
[46]Bhattacharya, A., Jadavpur Univ., Kolkata. Biogeography-Based Optimization for Different Economic Load Dispatch Problems [J]. IEEE Trans Power Syst,2010,25(2):1064-1077.
[47]万文军,周克毅,胥建群,等.动态系统实现火电厂机组负荷优化分配[J].中国电机工程学报,2005,25(2)：125-129.
[48]Qing A. Dynamic differential evolution strategy and applications in electromagnetic inverse scattering problem [J]. IEEE Transactions on Geosciences and Remote Sensing,2006,44(1): 116-125.
[49]Wan Wenjun, Zhou Keyi, Xu Jianqun, et al. Dynamic system on economic dispatch among thermal power units [J]. Proceedings of the CSEE,2005,25(2):125-129.
[50]郭斌,康松.火电厂各机组间负荷调度实时优化自动控制系统的研究[J].发电设备,2001,6：26-30.
[51]王友,马晓茜,刘翱.自动发电控制下的火电厂厂级负荷优化分配[J].中国电机工程学报,2008,28(14)：103-107.
[52]孙闻,房大中.考虑系统可靠性和经济性的机组组合方法[J].电网技术,2008,32(6)：47-51.
[53]刘丽平,叶春,忻建华.电厂在线性能分析及故障诊断系统[J].热力发电,2003,11：76-78.
[54]杨波,叶春,何锐盛,忻建华.火电厂在线性能分析和组态监测系统[J].电力自动化设备,2002,22(6)：45-46.
[55]耗润田,刘彦丰,高建强.机组间在线负荷优化模型及其应用[J].汽轮机技术,2008,50(1)：62-64.
[56]诸佩敏,忻建华.基于BP网络方法的热电厂性能分析系统[J].上海电力,2007,5：472-474.
[57]胡建军,李嘉龙,陈慧坤,卢恩,王一.基于煤耗和排放的日发电曲线编制模型[J].电力系统自动化,2009,33(12)：43-46.
[58]闫天明,郝润田.基于一体化模型仿真系统的单元机组煤耗特性处理模型[J].锅炉技术,2008,39(1)：22-24.
[59]叶涛.热力发电厂[M].2006,8,第2版,北京：中国电力出版,100-110.
[60]M. R. ALRASHIDI, K. M. EL-NAGGAR, A. K. AL-OTHMAN. Particle Swarm Optimization Based Approach for Estimating the Fuel-cost Function Parameters of Thermal Power Plants with Valve Loading Effects [J]. Electric Power Components and Systems, 2009,37(11):1219-1230.
[61]Daycock, C., DesJardins, R., and Fennell, S.. Generation cost forecasting using on-line thermo dynamics models [J]. Elect. Power,2004,1:1-9.
[62]Adhinarayanan Theerthamalai, Sydulu Maheswarapu. An effective non-iterative “k-logic based” algorithm for economic dispatch of generators with cubic fuel cost function [J]. Electrical Power and Energy Systems,2010,32:539-542.
[63]LeeK.Y, Sode-YomeA, June Ho Park. Adaptive Hopfield Neural Network for Economic Load Dispatch [J].IEEE Transaction on Power System,1998,13(2):519-526.
[64]Lalit Chandra Saikia, Sukumar Mishra, Nidul Sinha, J. Nanda. Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller [J]. Electric Power & Energy Systems.2011,33(4):1101-1108.
[65]Z.-L.Gaing. Particle swarm optimization to solving the economic dispatch considering the generator constraints [J]. IEEE Trans. Power Syst.,2003,18(3):1187-1195.
[66]A. I. Selvakumar, K. Thanushkodi. A new particle swarm optimization solution to nonconvex economic dispatch problems [J]. IEEE Trans. Power Syst.,2007,22(1):42-51.
[67]Jong-Bae Park.A particle swarm optimization for economic dispatch with nonsmooth cost functions [J]. IEEE Transactions onPower Systems,2005,20(1):34-42.
[68]Rani C, Rajesh Kumar M, Pavan K. Multi-Objective Generation Dispatch Using Particle Swarm Optimisation[C].2006 India International Conference on Power Electronics, IICPE, 2006,7:421-424.
[69]Jeyakumar DN, Jayabarathi T, Raghunathan T. Particle swarm optimization for various types of economic dispatch problems [J], Electric Power Syst.2006,28(1):36-42.
[70]Coelho Leandro dos Santos, Lee Chu-Sheng. Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches [J]. Int J Electr Power Energy Syst,2008,30(5):297-307.
[71]余廷芳,林显敏,林中达.遗传算法在火电厂机组负荷优化分配问题中的参数选择[J].汽轮机技术,2007,49(03)：217-219.
[72]孙力勇,张焰,蒋传文.基于矩阵实数编码遗传算法求解大规模机组组合问题[J].中国电机工程学报,2006,26(2)：82-87.
[73]K.S.Swarup, S.Yamashiro. Unit commitment solution methodologies using genetic algorithm [J], IEEE Trans. Power Systems,2002,17(2):87-91.
[74]A.G. Bakirtzis, P.Z.Biskas, C.E.Zoumas, and V. Petridis, Optimal power flow by enhance genetic algorithm [J], IEEE Trans. Power Systems,2002,17(5):229-236.
[75]Damousis loannis G, Bakirtzis Anastasios G, Dokopoulos Petros S. Network-constrained economic dispatch using real coded gentetic algorithm [J]. IEEE Trans Power System, 2002,18(1):198-205.
[76]Arroyo JM, Conejo AJ. A parallel repair genetic algorithm to solve the unit commitment problem [J]. IEEE Trans Power Syst.2002,17(4):1216-1224.
[77]Swarup KS, Yamashiro S. Unit commitment solution methodology using genetic algorithm [J]. IEEE Trans Power System.2003,17(1):87-91.
[78]Damousis IG, Bakirtzis AG, Dokopoulos PS. A solution to the unit commitment problem using integer coded genetic algorithm [J]. IEEE Trans Power System.2004,19(2): 1165-1172.
[79]N. Amjady H, Nasiri-Rad. Economic dispatch using an efficientreal-coded genetic algorithm [J]. IET Generation Transmission & Distribution,2009,3(3):266-278.
[80]Chao-Lung Chiang. Improved Genetic Algorithm for Power Economic Dispatch of Units With Valve-Point Effects and Multiple Fuels [J]. IEEE Trans Power System,2005,20(4): 1690-1699.
[81]C.L. Chiang, Genetic-based algorithm for power economic load dispatch [J]. IET Gener. Transom. Diatribe.2007,1(2):261-269.
[82]王小平,曹立明,遗传算法——理论、应用及软件实现[M].2002,1,第1版,西安：西安交通大学出版社,85-90.
[83]张国忠.智能控制系统及应用[M].2007,12,第1版,北京：中国电力出版社,95-100.
[84]Xiao Lion, Lv Shengping. An improved genetic algorithm for integrated process planning and scheduling [J]. Advanced Manufacturing Technology,2012,58(1):724-740.
[85]James C. Chen, Cheng-Chun Wu, Chia-Wen Chen, Kou-Huang Chen. Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm [J]. Expert Systems with Applications,2012,39(11):10016-10021.
[86]Nima Amjady, Hadi Nasiri-Rad. Nonconvex Economic Dispatch With AC Constraints by a New Real Coded Genetic Algorithm [J]. IEEE Trans Power System,2009,24(3): 1489-1502.
[87]S. H. Ling, F. H. F. Leung. An Improved genetic algorithm with average-bound crossover and wavelet mutation operations [J]. Soft Comput.,2007,11(1):7-31.
[88]Chiang, Ch.. Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels [J].IEEE Trans. Power Syst.,2005,20(4):1690-1699.
[89]Worawat Sa-ngiamvibool, Saravuth Pothiya, Issarachai Ngamroo. Multiple tabu search algorithm for economic dispatch problem considering valve-point effects [J]. Electrical Power & Energy Systems,2011,33(4):846-854.
[90]Pezzella F, Merelli E. A tabu search method guided by shifting bottleneck for the job shop scheduling problem [J]. European Journal of Operational Research,2000,120(2):297-310.
[91]黄志,黄文奇.一种基于禁忌搜索方法的作业车间调度[J].华中科技大学学报(自然科学版),2005,33(12)：109-111.
[92]肖丽,刘光远,贺一等.基于禁忌搜索的模糊神经网络结构优化[J].计算机科学,2006,33(7)：217-219.
[93]Chelouah R, Siarry P. Tabu Search applied to global optimization [J].European Journal of Operational Research,2000,123:256-170.
[94]Wang M, Chen X, Qian J.An improvement of continuous tabu search for global optimization [A].The 5th World Congress on Intelligent Control and Automation [C].2004, 1:375-377.
[95]Glover F. Tabu search—Part Ⅰ [J]. ORSA J. Computing,1989,1 (3):190—206.
[96]Glover F. Tabu Search—Part Ⅱ [J]. ORSA J. Computing,1990,2(1):4-32.
[97]路景,周春燕.基于遗传算法的混合优化策略研究[J].计算机技术与发展,2007,17(3)：145-149.
[98]徐好芹,贾延明.智能算法及其混合优化策略研究[J].软件导刊,2011,10(9)：48-50.
[99]Bhattacharya, A. Hybrid Differential Evolution With Biogeography-Based Optimization for Solution of Economic Load Dispatch [J]. IEEE Trans Power System,2010,25(4): 1955-1964.
[100]Jia-Chu Lee, Whei-Min Lin, Gwo-Ching Liao, Ta-Peng Tsao. Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system [J]. Electrical Power & Energy Systems,2011,33(2):189-197.
[101]龚常淡.混合遗传算法的应用研究[J].长春理工大学学报,2009,4(1)：131-132.
[102]Thibaut Vidal, Teodor Gabriel Crainic, Michel Gendreau, Nadia Lahrichi, Walter Rei. A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems [J]. OPERATIONS RESEARCH,2012,60(3):611-624.
[103]Xiao-Min Hu, Hybrid Genetic Algorithm Using a Forward Encoding Scheme for Lifetime Maximization of Wireless Sensor Networks [J]. IEEE Transactions on Evolutionary Computation,2010,14(5):766-781.
[104]Yi Zhang, Xiaoping Li, Qian Wang. Hybrid genetic algorithm for permutation flow shop scheduling problems with total flow time minimization [J]. European Journal of Operational Research,2009,196(3):869-876.
[105]周国华,武振业.一类Flow Shop排序问题的混合遗传算法[J].管理科学学报,1998,1(4)：20-25.
[106]梁旭,黄明.禁忌并行遗传算法在作业车间调度中的应用[J].计算机集成制造系统,2005,11(5)：6-9.
[107]柴永生,孙树栋,余建军,吴秀丽.基于免疫遗传算法的车间动态调度[J].2005,(10)：19-23.
[108]Dakuo He, Fuli Wang, Zhizhong Mao:A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect [J]. Electrical Power and Energy Systems [J].2008,30(1):31-38.
[109]Kumarappan N, Mohan M R. Neuro-hybrid genetic algorithms based economic dispatch for utility system[C]. Proceedings of the International Joint Conference on Neural Networks, Portland, United States,2003,7:2112-2117.
[110]J.S. Alsumait, J.K.Sykulski, A.K.Al-Othman:A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems [J]. Applied Energy.2010,87(5): 1773-1981.
[111]Yalcinoz T, Altun H. Power economic dispatch using a hybrid genetic algorithm [J]. IEEE Power Eng Rev.2001,21(3):59-60.
[112]Aniruddha Bhattacharya. Hybrid Differential Evolution with Biogeography-Based Optimization for Solution of Economic Load Dispatch [J]. IEEE TRANSACTIONS ON POWER SYSTEMS.2010,25(4):1955-1964
[113]T. Aruldoss Albert Victoire, A. Ebenezer Jeyakumar. A modified hybrid EP-SQP approach for dynamic dispatch with valve-point effect [J]. Electrical Power and Energy Systems. 2005,27(8):594-601
[114]K. Vaisakh, L.R. Srinivas. Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis [J]. Expert Systems with Applications.2011,38(3): 2046-2062
[115]Attaviriyanupap P, Kita H, Tanska E, Hasegawa J. A hybrid EP and SQP for dynamic economic dispatch with nonsmooth incremental fuel cost function [J]. IEEE Trans Power System.2002,17(2):411-416.
[116]Aruldoss Albert Victoire T, Ebenezer Jeyakumar A. A modified hybrid EP-SQP approach for dynamic dispatch with valve-point effect [J]. Electric Power Energy System,2005, 27(8):594-601.
[117]G. K.Purushothama, L. Jenkins. Simulated annealing with local search—A hybrid algorithm for unit commitment [J].IEEE Trans. Power System,2003,18(1):273-278.
[118]Attaviriyanupap P., Kita, H.,Tanaka,E., Hasegawa,J.. A hybrid EP and SQP for dynamic economic dispatch with non smooth fuel cost function [J]. IEEE Trans. Power Syst.,2002, 17(2):411-416.
[119]T. A. A. Victoire, A. E. Jeyakumar. Hybrid PSO-SQP for economic dispatch with valve-point effect [J], Electric Power Systems Research,2004,71(1):51-59.
[120]L. dos Santos Coelho, V. C. Mariani, Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect [J], IEEE Transactions on Power Systems,2006,21(2):989-996.
[121]Yutian Liu, Li Ma, Jianjun Zhang. Reactive power optimization by GA/SA/TS combined algorithms [J]. Electrical Power and Energy Systems,2004,24(9):765-769.
[122]S.H.Ling, H.C.Iu, K.Y.Chan, H.K.Lam. Hybrid particle swarm optimization with wavelet mutation and its industrial applications [J]. IEEE Trans. Syst.,2008,38(3):743-763.
[123]Ruangpayoonsak, N., Ongsakul, W., Runggeratikul, S.Constrained economic dispatch by combined genetic and simulated annealing algorithm [J]. Int. J.Electr.PowerComp.Syst., 2002,30(9):917-931.
[124]Whei-Min Lin, Fu-Sheng Cheng, Ming-Tong Tsay:An Improved Tabu Search for Economic Dispatch With Multiple Minima [J]. IEEE Transaction on power systems,2002,17(1): 108-112.
[125]W. Ongsakul, S. Dechanupaprittha, I. Ngamroo. Parallel tabu search algorithm for constrained economic dispatch [J]. Generation, Transmission and Distribution, IEE Proceedings,2004,151(2):157-166.
[126]C. Christober Asir Rajan, M. R. Mohan. An Evolutionary Programming-Based Tabu Search Method for Solving the Unit Commitment Problem [J]. IEEE Transactions on Power Systems,2004,19(1):577-585.
[127]Nidul sinha, R. Chakrabarti, and P.K. Chattopadhyay. Evolutionary Programming Techniques for Economic Load Dispatch [J]. IEEE Transactions on Computation,2003,7(l): 83-94
[128]Mantawy AH, Abdel-Magid YL, Selim SZ. Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem [J]. IEEE Trans Power Syst.1999, 14(3):829-836.
[129]Sudhakaran M, Slochanal S M R. Integrating genetic algorithms and tabu search for combined heat and power economic dispatch [C]. Proceeding of Conference on Convergent Technologies for Asia-Pacific Region,2003,1:67-71.
[130]孙艳丰.基于遗传算法和禁忌搜索算法的混合策略及其应用[J].北京工业大学学报,2006,32(3)：258-262.
[131]王淑玲,邢棉,李振涛,等.遗传禁忌神经网络模型及其在电力系统预测中的应用[J].华东电力,2007,35(2)：96-98.
[132]Jun-Qing Li, Quan-Ke Pan, P. N. Suganthan, T. J. Chua. A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem [J]. Advanced Manufacturing Technology,2011,52(5):683-697.
[133]李智勇,陈友文.一种融入小生境技术的遗传禁忌算法[J].湖南大学学报(自然科学版),2010,37(4)：81-84.
[134]GLOVER F, KELLY J, LAGUNA M. Genetic algorithm and tabu search:hybrids for optimizations [J]. Computers andOperations Research,1995,22 (1):111-134.
[135]朱永利,陈英伟,韩凯.基于改进的遗传禁忌搜索算法求解电力线路最佳抢修路径[J].信息化纵横,2009,6：58-62.
[136]李蔚,陈坚红,盛德仁,等.机组负荷优化的遗传禁忌混合算法[J].浙江大学学报：工学版,2007,41(11)：1862-1865.
[137]王林川,梁栋,于东皓,等.基于遗传和禁忌搜索混合算法的配电网重构[J].电力系统保护与控制,2009,37(6)：27-31.
[138]郑金华.多目标进化算法及其应用[M].2007,2,第1版,科学出版社,15-30.
[139]崔逊学.多目标进化算法及其应用[M].2006,1,第1版,国防工业出版社,23-40.
[140]崔逊学,林闯,方廷建.多目标进化算法的研究与进展[J].模式识别与人工智能,2003,16(3)：306-314.
[141]Li Xuebin. Study of multi-objective optimization and multi-attribute decision-making for economic and environmental power dispatch [J].Electric Power Systems Research.2009, 79(5):789-795.
[142]吴亮红,王耀南,袁小芳,张剑.多目标优化问题的差分进化算法研究[J].湖南大学学报,2009,36(2)：53-57.
[143]邹谊,魏文龙,李斌等.多目标量子编码遗传算法[J].电子与信息学报,2007,29(11)：2688-2692.
[144]LaumannsM, ThieleL, DebK, ZitzlerE. Combining convergence and diversity in evolutionary multi-objective optimization [J]. Evolutionary Computation.2002,10(3): 263-282.
[145]Hernandez Diaz A G, etal. Pareto-adaptive ε-dominance [J]. Evolutionary Computation. 2007,15(4):49-517.
[146]Carlos Artemio Coello. Evolutionary multiobjective optimization:A historical view of the field [J].IEEE Computational Intelligence Magazine,2006,1(1):28-36.
[147]E. Zio, P. Baraldi, N. Pedroni. Optimal power system generation scheduling by multi-objective genetical algorithms with preferences [J]. Reliability Engineering and System Safety,2009,94(2):432-444.
[148]M.A. Abido. A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch [J]. Electrical Power and Energy Systems.2003,25(2):97-105.
[149]RughooPuth HCS. Robert TF Ah King. Environmental/economic dispatch of thermal units using an elitist multiobjective evolutionary algorithm[C].2003 IEEE International Conference on Industrial Technology,2003,1:48-53.
[150]Tankut Yalcinoz, Onur Koksoy. A multi-objective optimization method to environmental economic dispatch [J]. Electrical Power and Energy Systems.2007,29(1):42-50.
[151]Li Xuebin. RETRACTED:Study of multi-objective optimization and multi-attribute decision-making for economic and environmental power dispatch [J]. Electric Power Systems Research,2009,79(5):789-795.
[152]Lingfeng Wang, Chanan Singh. Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm [J]. Electric Power Systems Research. 2007,77(12):1654-1664.
[153]Jiejin Caia, Xiaoqian Mab, Qiong Lic, Lixiang Lid, Haipeng Pengd. A multi-objective chaotic ant swarm optimization for environmental/economic dispatch [J]. Electrical Power & Energy Systems,2010,32(5):337-344.
[154]Chariklia A. Georgopoulou, Kyriakos C. Giannakoglou. Two-level, two-objective evolutionary algorithms for solving unit commitment problems [J]. Applied Energy.2009, 86(7-8):1229-1239.
[155]BlaZe Gjorgieva, Marko Cepinb. A multi-objective optimization based solution for the combined economic-environmental power dispatch problem [J]. Engineering Applications of Artificial Intelligence,2013,26(1):417-429
[156]冯士刚,艾芊.利用强度Pareto进化算法的多目标无功优化[J].高电压技术,2007,33(9)：115-119.
[157]Branke J, Kaubler T, Schmeck H. Guidance in evolutionary multi-objective optimization [J]. Adv Eng Software,2001,32(6):499-507.
[158]Abido M.A. Multiobjective evolutionary algorithms for electric power dispatch problem [J]. IEEE Trans Evol Comput,2006,10(3):315:329.
[159]Deb K. Solving goal programming problems using multi-objective genetic algorithms [J]. Congress Evolut Comput IEEE,1999,1:77-84.
[160]Coello CAC. Handling preferences in evolutionary multi-objective optimization [J]. Congress on evolutionary computation, New York:IEEE,2000,1:30-37.
[161]Cvetkovic D, Parmee IC. Preferences and their application in evolutionary multi-objective optimization [J]. IEEE Trans Evol Comput.2002,6(1):40-48.
[162]王一,程浩忠.计及输电阻塞的帕累托最优多目标电网规划[J].中国电机工程学报,2008,28(13)：132-138.
[163]Leung Y W, Wang Y P. U-measure:A Quality Measure for Multiobjective Programming [J]. IEEE Transactions on systems, man and cybernetics,2003,33(3):337-343.
[164]王勇,蔡自兴,周育人,肖赤心.约束优化进化算法[J].软件学报,2009,20(1)：11-29.
[165]周育人,李元香,王勇,康立山.Pareto强度值演化算法求解约束优化问题[J].软件学报,2003,14(7)：1243-1249.
[166]Mezura-Montes E, Coello Coello CA. A simple multimembered evolution strategy to solve constrained optimization problems [J]. IEEE Trans. on Evolutionary Computation,2005, 9(1):1-17.
[167]RunarssonTP, Yao X.Search biases in constrained evolutionary optimization [J].IEEE Trans.on Systems, Man, Cybernetics(C),2005,35(2):233-243.
[168]Cai Z, Wang Y.A multiobjective optimization based evolutionary algorithm for constrained optimization [J].IEEE Trans. OnEvolutionary Computation,2006,10(6):658-675.
[169]Venkatraman S, Yen GG.A generic framework for constrained optimization using genetic algorithms [J].IEEE Trans.on EvolutionaryComputation,2005,9(4):424-435.
[170]Deb K, Pratab A, Agrawal S, Meyarivan T. A fast and elitist nondominated sorting genetic algorithm for multi-objective optimization:NSGA Ⅱ [J]. IEEE Trans.on Evolutionary Computation,2002,6(2):182-197.
[171]Tapabrata Ray, Hemant Kumar Singh, Amitay Isaacs, Warren Smith. Infeasibility Driven Evolutionary Algorithm for Constrained Optimization [J]. Constraint-Handling in Evolutionary Optimization.2009,198:145-165.
[172]Coello Coello CA, Mezura-Montes E.Constraint-Handling in genetic algorithms through the use of dominance-based tournamentselection [J].Advanced Engineering Informatics,2002, 16(3):193-203.
[173]B.Y. Qu, P.N. Suganthan. Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection [J]. Information Sciences,2010,180(17): 3170-3181.
[174]Wang Y,Cai ZX,Guo GQ,Zhou YR.Multiobjective optimization and hybrid evolutionary algorithm to solve constrainedoptimization problems [J]. IEEE Trans. on Systems, Man and Cybernetics(B),2007,37(3):560-575.
[2]陆延昌,21世纪初期中国电力工业展望,中国电力,2000,33(7)：1-8.
[3]王承民,于尔铿,郭志忠.电力市场中一种基于动态规划法的经济负荷分配算法[J].电力系统自动化,2000,21：89-93.
[4]Santos J R, Lora A T, Exposito A G, et al. Finding improved local minima of power system optimization problems by interior-point methods [J]. IEEE Transactions on power systems, 2003,18(1):238-244.
[5]R. A. Jabr, A. H. Coonick, B. J. Cory. A homogeneous linear programming algorithm for the security constrained economic dispatch problem [J]. IEEE Trans. Power Syst.,2000,15(3): 930-936.
[6]Daneshi C, hoobbari A.L, Shahidehpour M Mixed integer programming method to solve security constrained unit commitment with restricted operating zone limits [C]. IEEE International Conference onElectro/Information Technology,2008,5:187-192.
[7]A. G. Bakirtzis, C. E. Zoumas, Lambda of Lagrangian relaxation solution to unit commitment problem [J]. Proc. Inst. Elect. Eng. Gen.Transm. Dist.,2000,147(2):131-136.
[8]Coelho Ld.S, Mariani V.C. Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect [J]. IEEE Transactions on Power Systems,2006,21(2):989-996.
[9]刘自发,张建华.一种求解电力经济负荷分配问题的改进微分进化算法[J].中国电机工程学报,2008,28(10)：100-105.
[10]Singhal, P.K.Dynamic programming approach for solving power generating unit commitment problem [C].Computer and Communication Technology (ICCCT)2011 2nd International Conference.2011,9:298-303.
[11]Momoh JA, El-Hawary ME, Adapa R. A review of selected optimal power flow literature to 1993 part I:nonlinear and quadratic programming approaches [J]. IEEE Trans Power Syst, 1999,14(1):96-104.
[12]Momoh JA, El-Hawary ME, Adapa R. A review of selected optimal power flow literature to 1993 Part ll:Newton, linear programming and interior pointmethods [J]. IEEE Trans Power Syst,1999,14(1):105-11.
[13]Amit Kumar, Jagdeep Kaur, Pushpinder Singh.A new method for solving fully fuzzy linear programming problems [J]. Applied Mathematical Modelling.2011,35(2):817-823.
[14]Waight J.G, Bose A, Sheble G.B. Generation Dispatch with Reserve Margin Constraints Using Linear Programming [J]. IEEE Transactions on Power Apparatus and Systems.1981, 100(1):252-258.
[15]SimopoulosD.N, Kavatza S.D.Reliability Constrained Unit Commitment Using Simulated Annealing [J]. IEEE Transactions on Power Systems,2006,21(04):1699-1706.
[16]A. Viana, J. P. Sousa, and M. Matos, Simulated annealing for the unit commitment problem, the IEEE Porto Power Tech Conf [C],2001,9:10-13.
[17]Holland J H. Genetic Algorithms [J]. Scientific American,1992,267(1):44-50.
[18]Yu Tingfang, Lin Zhongda. Application of float genetic algorithms-partially solved combined with punishing function in power plant units commitment problem [J]. Proceedings of the CSEE,2009,29(2):107-110.
[19]Dang C, Li M. A floating-point genetic algorithm for solving the unit commitment problem [J]. Eur J Oper Res.2007,187:1370-1395.
[20]Chiang C L. Genetic algorithms for power economic [J]. IET on generation transmission and distribution,2007, 1(2):261-269.
[21]CHEN Yan-qiao, NI Min, LIU Ji-zhen, WEI Xiang-guo. Application of Real-code Genetic Algorithm to Economic Load Dispatch in Power Plants [J]. Proceedings of the CSEE,2007, 27(20):107-112.
[22]David C, Gerald B. Genetic algorithm solution of economic dispatch with valve point loading [J]. IEEE Transactions on Power Systems,1993,8(3):1325-1332.
[23]闫顺林,李太兴,刘振刚.遗传算法搜索优化及其在机组负荷优化分配中的应用[J].东北电力技术,2007,28(10)：20-22.
[24]JAndre, PSiarry, TDognon. An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization [J]. Advance in Engineering Software, 2001,32(1):49-60.
[25]叶正华,谢勇,郑金华.一种改进的基于实数编码的遗传算法[J].湘潭大学自然科学学报,2002,24(3)：32-35.
[26]余廷芳,林中达,林显敏.部分解约束改进遗传算法在火电厂机组负荷优化分配中的应用[J].汽轮机技术,2006,48(5)：352-355.
[27]方彦军,伍洲,王琛.集散遗传算法在厂级AGC负荷分配中的应用[J].电网技术,2010,7(34)：190-194.
[28]Wona Jong-Ryul, Park Young-Moon. Economic dispatch solutions with piecewise quadratic cost function using improved genetic algorithm [J]. Electric Power Energy Syst.2003,25 (5):355-361.
[29]倪敏,陈彦桥,刘吉臻.基于遗传算法的火电机组负荷优化分配研究[J].华北电力大学学报,2006,33(5)：65-67.
[30]谈英姿,沈炯,吕震中,免疫优化算法及其前景展望,信息与控制,2002,31(5)：385-390
[31]Cutello V, Nicosia G., Romeo M, Oliveto P.S. On the Convergence of Immune Algorithms [C]. Foundations of Computational Intelligence, FOCI 2007, IEEE Symposium.2007,4: 409-415.
[32]Na-Na Li, Yong-Feng Dong, Jun-Hua Gu, Rui-Ying Zhou. A New Algorithm Based on Immune Algorithm and Hopfield Neural Network for Multimodal Function Optimization [C]. Machine Learning and Cybernetics,2007 International Conference on.2007,8: 2837-2840.
[33]李蔚,刘长东,盛德仁,陈坚红,袁镇福,岑可法,免疫算法在火电机组优化组合中的应用,浙江大学学报(工学版),2004,38(8)：1090-1094
[34]Taher Niknam.A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem [J].Applied Energy. 2010,87(1):327-339.
[35]D. N. Jeyakumar, T. Jayabarathi, T. Raghunathan. Particle swarm optimization for various types of economic dispatch problems [J], International Journal of Electrical Power & Energy Systems,2006,28(1):36-42.
[36]SriyanyongP, SongY.H. Unit commitment using particle swarm optimization combined with Lagrange relaxation[C].2005 IEEE Power Engineering Society General Meeting,2005,6: 2752-2759.
[37]AlRashidi, M. R.,El-Hawary,M. E.. A survey of particle swarm optimization applications in electric power systems [J]. IEEE Trans. Evolution Commutate,2009,13 (4):913-918.
[38]Hou Yunhe, Lu Lijuan, Xiong Xinliang, et al. Enhanced particle swarm optimal algorithm and its application on one economic dispatch of power system [J]. Proceedings of the CSEE, 2004,24(7):95-100.
[39]J.-B. Park, K.-S. Lee, J.-R. Shin, K. Y. Lee. A particle swarm optimization for economic dispatch with nonsmooth cost functions [J], IEEE Trans. Power Syst.,2005,20(1):34-42.
[40]侯云鹤,鲁丽娟,熊信良等.改进粒子群及其在电力系统经济负荷分配中的应用[J].中国电机工程学报,2004,24(7)：95-100.
[41]修春波,陆丽芬.改进的混沌优化算法及其在电力系统经济负荷分配中的应用研究[J].电力系统保护与控制,2010,38(21)：109-112.
[42]唐巍,李殿璞.电力系统经济负荷分配的混沌优化方法[J].中国电机工程学报,2000,20(10)：36-40.
[43]廖艳芬,马晓茜.改进的混沌优化方法化电站机组负荷分配中的应用[J].动力工程,2006,26(1)：93-96.
[44]下爽心,韩芳,朱衡君.基于改进变尺度混沌优化方法的经济负荷分配[J].中国电机工程学报,2005,25(24)：90-95.
[45]J Nanda J, Mishra S, Saikia Lalit Chandra. Maiden application of bacterial foraging based optimization technique in multiarea automatic generation control [J]. IEEE Trans Power Syst,2009,24(2):602-609
[46]Bhattacharya, A., Jadavpur Univ., Kolkata. Biogeography-Based Optimization for Different Economic Load Dispatch Problems [J]. IEEE Trans Power Syst,2010,25(2):1064-1077.
[47]万文军,周克毅,胥建群,等.动态系统实现火电厂机组负荷优化分配[J].中国电机工程学报,2005,25(2)：125-129.
[48]Qing A. Dynamic differential evolution strategy and applications in electromagnetic inverse scattering problem [J]. IEEE Transactions on Geosciences and Remote Sensing,2006,44(1): 116-125.
[49]Wan Wenjun, Zhou Keyi, Xu Jianqun, et al. Dynamic system on economic dispatch among thermal power units [J]. Proceedings of the CSEE,2005,25(2):125-129.
[50]郭斌,康松.火电厂各机组间负荷调度实时优化自动控制系统的研究[J].发电设备,2001,6：26-30.
[51]王友,马晓茜,刘翱.自动发电控制下的火电厂厂级负荷优化分配[J].中国电机工程学报,2008,28(14)：103-107.
[52]孙闻,房大中.考虑系统可靠性和经济性的机组组合方法[J].电网技术,2008,32(6)：47-51.
[53]刘丽平,叶春,忻建华.电厂在线性能分析及故障诊断系统[J].热力发电,2003,11：76-78.
[54]杨波,叶春,何锐盛,忻建华.火电厂在线性能分析和组态监测系统[J].电力自动化设备,2002,22(6)：45-46.
[55]耗润田,刘彦丰,高建强.机组间在线负荷优化模型及其应用[J].汽轮机技术,2008,50(1)：62-64.
[56]诸佩敏,忻建华.基于BP网络方法的热电厂性能分析系统[J].上海电力,2007,5：472-474.
[57]胡建军,李嘉龙,陈慧坤,卢恩,王一.基于煤耗和排放的日发电曲线编制模型[J].电力系统自动化,2009,33(12)：43-46.
[58]闫天明,郝润田.基于一体化模型仿真系统的单元机组煤耗特性处理模型[J].锅炉技术,2008,39(1)：22-24.
[59]叶涛.热力发电厂[M].2006,8,第2版,北京：中国电力出版,100-110.
[60]M. R. ALRASHIDI, K. M. EL-NAGGAR, A. K. AL-OTHMAN. Particle Swarm Optimization Based Approach for Estimating the Fuel-cost Function Parameters of Thermal Power Plants with Valve Loading Effects [J]. Electric Power Components and Systems, 2009,37(11):1219-1230.
[61]Daycock, C., DesJardins, R., and Fennell, S.. Generation cost forecasting using on-line thermo dynamics models [J]. Elect. Power,2004,1:1-9.
[62]Adhinarayanan Theerthamalai, Sydulu Maheswarapu. An effective non-iterative “k-logic based” algorithm for economic dispatch of generators with cubic fuel cost function [J]. Electrical Power and Energy Systems,2010,32:539-542.
[63]LeeK.Y, Sode-YomeA, June Ho Park. Adaptive Hopfield Neural Network for Economic Load Dispatch [J].IEEE Transaction on Power System,1998,13(2):519-526.
[64]Lalit Chandra Saikia, Sukumar Mishra, Nidul Sinha, J. Nanda. Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller [J]. Electric Power & Energy Systems.2011,33(4):1101-1108.
[65]Z.-L.Gaing. Particle swarm optimization to solving the economic dispatch considering the generator constraints [J]. IEEE Trans. Power Syst.,2003,18(3):1187-1195.
[66]A. I. Selvakumar, K. Thanushkodi. A new particle swarm optimization solution to nonconvex economic dispatch problems [J]. IEEE Trans. Power Syst.,2007,22(1):42-51.
[67]Jong-Bae Park.A particle swarm optimization for economic dispatch with nonsmooth cost functions [J]. IEEE Transactions onPower Systems,2005,20(1):34-42.
[68]Rani C, Rajesh Kumar M, Pavan K. Multi-Objective Generation Dispatch Using Particle Swarm Optimisation[C].2006 India International Conference on Power Electronics, IICPE, 2006,7:421-424.
[69]Jeyakumar DN, Jayabarathi T, Raghunathan T. Particle swarm optimization for various types of economic dispatch problems [J], Electric Power Syst.2006,28(1):36-42.
[70]Coelho Leandro dos Santos, Lee Chu-Sheng. Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches [J]. Int J Electr Power Energy Syst,2008,30(5):297-307.
[71]余廷芳,林显敏,林中达.遗传算法在火电厂机组负荷优化分配问题中的参数选择[J].汽轮机技术,2007,49(03)：217-219.
[72]孙力勇,张焰,蒋传文.基于矩阵实数编码遗传算法求解大规模机组组合问题[J].中国电机工程学报,2006,26(2)：82-87.
[73]K.S.Swarup, S.Yamashiro. Unit commitment solution methodologies using genetic algorithm [J], IEEE Trans. Power Systems,2002,17(2):87-91.
[74]A.G. Bakirtzis, P.Z.Biskas, C.E.Zoumas, and V. Petridis, Optimal power flow by enhance genetic algorithm [J], IEEE Trans. Power Systems,2002,17(5):229-236.
[75]Damousis loannis G, Bakirtzis Anastasios G, Dokopoulos Petros S. Network-constrained economic dispatch using real coded gentetic algorithm [J]. IEEE Trans Power System, 2002,18(1):198-205.
[76]Arroyo JM, Conejo AJ. A parallel repair genetic algorithm to solve the unit commitment problem [J]. IEEE Trans Power Syst.2002,17(4):1216-1224.
[77]Swarup KS, Yamashiro S. Unit commitment solution methodology using genetic algorithm [J]. IEEE Trans Power System.2003,17(1):87-91.
[78]Damousis IG, Bakirtzis AG, Dokopoulos PS. A solution to the unit commitment problem using integer coded genetic algorithm [J]. IEEE Trans Power System.2004,19(2): 1165-1172.
[79]N. Amjady H, Nasiri-Rad. Economic dispatch using an efficientreal-coded genetic algorithm [J]. IET Generation Transmission & Distribution,2009,3(3):266-278.
[80]Chao-Lung Chiang. Improved Genetic Algorithm for Power Economic Dispatch of Units With Valve-Point Effects and Multiple Fuels [J]. IEEE Trans Power System,2005,20(4): 1690-1699.
[81]C.L. Chiang, Genetic-based algorithm for power economic load dispatch [J]. IET Gener. Transom. Diatribe.2007,1(2):261-269.
[82]王小平,曹立明,遗传算法——理论、应用及软件实现[M].2002,1,第1版,西安：西安交通大学出版社,85-90.
[83]张国忠.智能控制系统及应用[M].2007,12,第1版,北京：中国电力出版社,95-100.
[84]Xiao Lion, Lv Shengping. An improved genetic algorithm for integrated process planning and scheduling [J]. Advanced Manufacturing Technology,2012,58(1):724-740.
[85]James C. Chen, Cheng-Chun Wu, Chia-Wen Chen, Kou-Huang Chen. Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm [J]. Expert Systems with Applications,2012,39(11):10016-10021.
[86]Nima Amjady, Hadi Nasiri-Rad. Nonconvex Economic Dispatch With AC Constraints by a New Real Coded Genetic Algorithm [J]. IEEE Trans Power System,2009,24(3): 1489-1502.
[87]S. H. Ling, F. H. F. Leung. An Improved genetic algorithm with average-bound crossover and wavelet mutation operations [J]. Soft Comput.,2007,11(1):7-31.
[88]Chiang, Ch.. Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels [J].IEEE Trans. Power Syst.,2005,20(4):1690-1699.
[89]Worawat Sa-ngiamvibool, Saravuth Pothiya, Issarachai Ngamroo. Multiple tabu search algorithm for economic dispatch problem considering valve-point effects [J]. Electrical Power & Energy Systems,2011,33(4):846-854.
[90]Pezzella F, Merelli E. A tabu search method guided by shifting bottleneck for the job shop scheduling problem [J]. European Journal of Operational Research,2000,120(2):297-310.
[91]黄志,黄文奇.一种基于禁忌搜索方法的作业车间调度[J].华中科技大学学报(自然科学版),2005,33(12)：109-111.
[92]肖丽,刘光远,贺一等.基于禁忌搜索的模糊神经网络结构优化[J].计算机科学,2006,33(7)：217-219.
[93]Chelouah R, Siarry P. Tabu Search applied to global optimization [J].European Journal of Operational Research,2000,123:256-170.
[94]Wang M, Chen X, Qian J.An improvement of continuous tabu search for global optimization [A].The 5th World Congress on Intelligent Control and Automation [C].2004, 1:375-377.
[95]Glover F. Tabu search—Part Ⅰ [J]. ORSA J. Computing,1989,1 (3):190—206.
[96]Glover F. Tabu Search—Part Ⅱ [J]. ORSA J. Computing,1990,2(1):4-32.
[97]路景,周春燕.基于遗传算法的混合优化策略研究[J].计算机技术与发展,2007,17(3)：145-149.
[98]徐好芹,贾延明.智能算法及其混合优化策略研究[J].软件导刊,2011,10(9)：48-50.
[99]Bhattacharya, A. Hybrid Differential Evolution With Biogeography-Based Optimization for Solution of Economic Load Dispatch [J]. IEEE Trans Power System,2010,25(4): 1955-1964.
[100]Jia-Chu Lee, Whei-Min Lin, Gwo-Ching Liao, Ta-Peng Tsao. Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system [J]. Electrical Power & Energy Systems,2011,33(2):189-197.
[101]龚常淡.混合遗传算法的应用研究[J].长春理工大学学报,2009,4(1)：131-132.
[102]Thibaut Vidal, Teodor Gabriel Crainic, Michel Gendreau, Nadia Lahrichi, Walter Rei. A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems [J]. OPERATIONS RESEARCH,2012,60(3):611-624.
[103]Xiao-Min Hu, Hybrid Genetic Algorithm Using a Forward Encoding Scheme for Lifetime Maximization of Wireless Sensor Networks [J]. IEEE Transactions on Evolutionary Computation,2010,14(5):766-781.
[104]Yi Zhang, Xiaoping Li, Qian Wang. Hybrid genetic algorithm for permutation flow shop scheduling problems with total flow time minimization [J]. European Journal of Operational Research,2009,196(3):869-876.
[105]周国华,武振业.一类Flow Shop排序问题的混合遗传算法[J].管理科学学报,1998,1(4)：20-25.
[106]梁旭,黄明.禁忌并行遗传算法在作业车间调度中的应用[J].计算机集成制造系统,2005,11(5)：6-9.
[107]柴永生,孙树栋,余建军,吴秀丽.基于免疫遗传算法的车间动态调度[J].2005,(10)：19-23.
[108]Dakuo He, Fuli Wang, Zhizhong Mao:A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect [J]. Electrical Power and Energy Systems [J].2008,30(1):31-38.
[109]Kumarappan N, Mohan M R. Neuro-hybrid genetic algorithms based economic dispatch for utility system[C]. Proceedings of the International Joint Conference on Neural Networks, Portland, United States,2003,7:2112-2117.
[110]J.S. Alsumait, J.K.Sykulski, A.K.Al-Othman:A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems [J]. Applied Energy.2010,87(5): 1773-1981.
[111]Yalcinoz T, Altun H. Power economic dispatch using a hybrid genetic algorithm [J]. IEEE Power Eng Rev.2001,21(3):59-60.
[112]Aniruddha Bhattacharya. Hybrid Differential Evolution with Biogeography-Based Optimization for Solution of Economic Load Dispatch [J]. IEEE TRANSACTIONS ON POWER SYSTEMS.2010,25(4):1955-1964
[113]T. Aruldoss Albert Victoire, A. Ebenezer Jeyakumar. A modified hybrid EP-SQP approach for dynamic dispatch with valve-point effect [J]. Electrical Power and Energy Systems. 2005,27(8):594-601
[114]K. Vaisakh, L.R. Srinivas. Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis [J]. Expert Systems with Applications.2011,38(3): 2046-2062
[115]Attaviriyanupap P, Kita H, Tanska E, Hasegawa J. A hybrid EP and SQP for dynamic economic dispatch with nonsmooth incremental fuel cost function [J]. IEEE Trans Power System.2002,17(2):411-416.
[116]Aruldoss Albert Victoire T, Ebenezer Jeyakumar A. A modified hybrid EP-SQP approach for dynamic dispatch with valve-point effect [J]. Electric Power Energy System,2005, 27(8):594-601.
[117]G. K.Purushothama, L. Jenkins. Simulated annealing with local search—A hybrid algorithm for unit commitment [J].IEEE Trans. Power System,2003,18(1):273-278.
[118]Attaviriyanupap P., Kita, H.,Tanaka,E., Hasegawa,J.. A hybrid EP and SQP for dynamic economic dispatch with non smooth fuel cost function [J]. IEEE Trans. Power Syst.,2002, 17(2):411-416.
[119]T. A. A. Victoire, A. E. Jeyakumar. Hybrid PSO-SQP for economic dispatch with valve-point effect [J], Electric Power Systems Research,2004,71(1):51-59.
[120]L. dos Santos Coelho, V. C. Mariani, Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect [J], IEEE Transactions on Power Systems,2006,21(2):989-996.
[121]Yutian Liu, Li Ma, Jianjun Zhang. Reactive power optimization by GA/SA/TS combined algorithms [J]. Electrical Power and Energy Systems,2004,24(9):765-769.
[122]S.H.Ling, H.C.Iu, K.Y.Chan, H.K.Lam. Hybrid particle swarm optimization with wavelet mutation and its industrial applications [J]. IEEE Trans. Syst.,2008,38(3):743-763.
[123]Ruangpayoonsak, N., Ongsakul, W., Runggeratikul, S.Constrained economic dispatch by combined genetic and simulated annealing algorithm [J]. Int. J.Electr.PowerComp.Syst., 2002,30(9):917-931.
[124]Whei-Min Lin, Fu-Sheng Cheng, Ming-Tong Tsay:An Improved Tabu Search for Economic Dispatch With Multiple Minima [J]. IEEE Transaction on power systems,2002,17(1): 108-112.
[125]W. Ongsakul, S. Dechanupaprittha, I. Ngamroo. Parallel tabu search algorithm for constrained economic dispatch [J]. Generation, Transmission and Distribution, IEE Proceedings,2004,151(2):157-166.
[126]C. Christober Asir Rajan, M. R. Mohan. An Evolutionary Programming-Based Tabu Search Method for Solving the Unit Commitment Problem [J]. IEEE Transactions on Power Systems,2004,19(1):577-585.
[127]Nidul sinha, R. Chakrabarti, and P.K. Chattopadhyay. Evolutionary Programming Techniques for Economic Load Dispatch [J]. IEEE Transactions on Computation,2003,7(l): 83-94
[128]Mantawy AH, Abdel-Magid YL, Selim SZ. Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem [J]. IEEE Trans Power Syst.1999, 14(3):829-836.
[129]Sudhakaran M, Slochanal S M R. Integrating genetic algorithms and tabu search for combined heat and power economic dispatch [C]. Proceeding of Conference on Convergent Technologies for Asia-Pacific Region,2003,1:67-71.
[130]孙艳丰.基于遗传算法和禁忌搜索算法的混合策略及其应用[J].北京工业大学学报,2006,32(3)：258-262.
[131]王淑玲,邢棉,李振涛,等.遗传禁忌神经网络模型及其在电力系统预测中的应用[J].华东电力,2007,35(2)：96-98.
[132]Jun-Qing Li, Quan-Ke Pan, P. N. Suganthan, T. J. Chua. A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem [J]. Advanced Manufacturing Technology,2011,52(5):683-697.
[133]李智勇,陈友文.一种融入小生境技术的遗传禁忌算法[J].湖南大学学报(自然科学版),2010,37(4)：81-84.
[134]GLOVER F, KELLY J, LAGUNA M. Genetic algorithm and tabu search:hybrids for optimizations [J]. Computers andOperations Research,1995,22 (1):111-134.
[135]朱永利,陈英伟,韩凯.基于改进的遗传禁忌搜索算法求解电力线路最佳抢修路径[J].信息化纵横,2009,6：58-62.
[136]李蔚,陈坚红,盛德仁,等.机组负荷优化的遗传禁忌混合算法[J].浙江大学学报：工学版,2007,41(11)：1862-1865.
[137]王林川,梁栋,于东皓,等.基于遗传和禁忌搜索混合算法的配电网重构[J].电力系统保护与控制,2009,37(6)：27-31.
[138]郑金华.多目标进化算法及其应用[M].2007,2,第1版,科学出版社,15-30.
[139]崔逊学.多目标进化算法及其应用[M].2006,1,第1版,国防工业出版社,23-40.
[140]崔逊学,林闯,方廷建.多目标进化算法的研究与进展[J].模式识别与人工智能,2003,16(3)：306-314.
[141]Li Xuebin. Study of multi-objective optimization and multi-attribute decision-making for economic and environmental power dispatch [J].Electric Power Systems Research.2009, 79(5):789-795.
[142]吴亮红,王耀南,袁小芳,张剑.多目标优化问题的差分进化算法研究[J].湖南大学学报,2009,36(2)：53-57.
[143]邹谊,魏文龙,李斌等.多目标量子编码遗传算法[J].电子与信息学报,2007,29(11)：2688-2692.
[144]LaumannsM, ThieleL, DebK, ZitzlerE. Combining convergence and diversity in evolutionary multi-objective optimization [J]. Evolutionary Computation.2002,10(3): 263-282.
[145]Hernandez Diaz A G, etal. Pareto-adaptive ε-dominance [J]. Evolutionary Computation. 2007,15(4):49-517.
[146]Carlos Artemio Coello. Evolutionary multiobjective optimization:A historical view of the field [J].IEEE Computational Intelligence Magazine,2006,1(1):28-36.
[147]E. Zio, P. Baraldi, N. Pedroni. Optimal power system generation scheduling by multi-objective genetical algorithms with preferences [J]. Reliability Engineering and System Safety,2009,94(2):432-444.
[148]M.A. Abido. A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch [J]. Electrical Power and Energy Systems.2003,25(2):97-105.
[149]RughooPuth HCS. Robert TF Ah King. Environmental/economic dispatch of thermal units using an elitist multiobjective evolutionary algorithm[C].2003 IEEE International Conference on Industrial Technology,2003,1:48-53.
[150]Tankut Yalcinoz, Onur Koksoy. A multi-objective optimization method to environmental economic dispatch [J]. Electrical Power and Energy Systems.2007,29(1):42-50.
[151]Li Xuebin. RETRACTED:Study of multi-objective optimization and multi-attribute decision-making for economic and environmental power dispatch [J]. Electric Power Systems Research,2009,79(5):789-795.
[152]Lingfeng Wang, Chanan Singh. Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm [J]. Electric Power Systems Research. 2007,77(12):1654-1664.
[153]Jiejin Caia, Xiaoqian Mab, Qiong Lic, Lixiang Lid, Haipeng Pengd. A multi-objective chaotic ant swarm optimization for environmental/economic dispatch [J]. Electrical Power & Energy Systems,2010,32(5):337-344.
[154]Chariklia A. Georgopoulou, Kyriakos C. Giannakoglou. Two-level, two-objective evolutionary algorithms for solving unit commitment problems [J]. Applied Energy.2009, 86(7-8):1229-1239.
[155]BlaZe Gjorgieva, Marko Cepinb. A multi-objective optimization based solution for the combined economic-environmental power dispatch problem [J]. Engineering Applications of Artificial Intelligence,2013,26(1):417-429
[156]冯士刚,艾芊.利用强度Pareto进化算法的多目标无功优化[J].高电压技术,2007,33(9)：115-119.
[157]Branke J, Kaubler T, Schmeck H. Guidance in evolutionary multi-objective optimization [J]. Adv Eng Software,2001,32(6):499-507.
[158]Abido M.A. Multiobjective evolutionary algorithms for electric power dispatch problem [J]. IEEE Trans Evol Comput,2006,10(3):315:329.
[159]Deb K. Solving goal programming problems using multi-objective genetic algorithms [J]. Congress Evolut Comput IEEE,1999,1:77-84.
[160]Coello CAC. Handling preferences in evolutionary multi-objective optimization [J]. Congress on evolutionary computation, New York:IEEE,2000,1:30-37.
[161]Cvetkovic D, Parmee IC. Preferences and their application in evolutionary multi-objective optimization [J]. IEEE Trans Evol Comput.2002,6(1):40-48.
[162]王一,程浩忠.计及输电阻塞的帕累托最优多目标电网规划[J].中国电机工程学报,2008,28(13)：132-138.
[163]Leung Y W, Wang Y P. U-measure:A Quality Measure for Multiobjective Programming [J]. IEEE Transactions on systems, man and cybernetics,2003,33(3):337-343.
[164]王勇,蔡自兴,周育人,肖赤心.约束优化进化算法[J].软件学报,2009,20(1)：11-29.
[165]周育人,李元香,王勇,康立山.Pareto强度值演化算法求解约束优化问题[J].软件学报,2003,14(7)：1243-1249.
[166]Mezura-Montes E, Coello Coello CA. A simple multimembered evolution strategy to solve constrained optimization problems [J]. IEEE Trans. on Evolutionary Computation,2005, 9(1):1-17.
[167]RunarssonTP, Yao X.Search biases in constrained evolutionary optimization [J].IEEE Trans.on Systems, Man, Cybernetics(C),2005,35(2):233-243.
[168]Cai Z, Wang Y.A multiobjective optimization based evolutionary algorithm for constrained optimization [J].IEEE Trans. OnEvolutionary Computation,2006,10(6):658-675.
[169]Venkatraman S, Yen GG.A generic framework for constrained optimization using genetic algorithms [J].IEEE Trans.on EvolutionaryComputation,2005,9(4):424-435.
[170]Deb K, Pratab A, Agrawal S, Meyarivan T. A fast and elitist nondominated sorting genetic algorithm for multi-objective optimization:NSGA Ⅱ [J]. IEEE Trans.on Evolutionary Computation,2002,6(2):182-197.
[171]Tapabrata Ray, Hemant Kumar Singh, Amitay Isaacs, Warren Smith. Infeasibility Driven Evolutionary Algorithm for Constrained Optimization [J]. Constraint-Handling in Evolutionary Optimization.2009,198:145-165.
[172]Coello Coello CA, Mezura-Montes E.Constraint-Handling in genetic algorithms through the use of dominance-based tournamentselection [J].Advanced Engineering Informatics,2002, 16(3):193-203.
[173]B.Y. Qu, P.N. Suganthan. Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection [J]. Information Sciences,2010,180(17): 3170-3181.
[174]Wang Y,Cai ZX,Guo GQ,Zhou YR.Multiobjective optimization and hybrid evolutionary algorithm to solve constrainedoptimization problems [J]. IEEE Trans. on Systems, Man and Cybernetics(B),2007,37(3):560-575.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |