电力系统中若干优化问题的研究
|
摘要
根据现代电力系统的特点和发展趋势,深入研究电力系统现代应用技术的特性,发展和完善现代电力系统优化模型和实用算法是当前电力系统研究和工程实践的重要课题之一。本论文针对现代电力系统优化中的若干问题及其实用算法进行了深入的研究,主要工作如下：
保证运行电压合格率和提高电压稳定性的前提下,为了使电网系统的有功网损最小,文中了建立了集安全性和经济性于一体的多目标无功优化模型。给出了解决多目标无功优化问题的多目标粒子群算法,针对Pareto最优解集中的解质量评价困难的问题,采用熵权决策法进行不同量纲的多目标优化整合,最终选择出满意的最优解。
提出了一种改进的人工免疫算法,算法中对质量不同的个体采用了不同的变异策略,对变异率和交叉率进行了自适应调整,使算法在搜索精度和搜索效率之间达到平衡。构造了基于DAIA算法的DAIA-BPNN电力系统短期负荷预测模型,利用DAIA算法来优化BPNN的权值和阈值,克服了BPNN权值和阈值选择的盲目性。电力系统短期负荷预测的实际算例表明,与人工神经网络及回归分析模型相比,本文所提出的方法具有更高的预测精度和鲁棒性。
以辽宁省电力有限公司线路检修计划为背景,根据图论中的图着色理论建立了电网检修优化模型,引入成本—效益分析概念,利用层次分析法加以分析研究,并提出了种改进的鱼群算法对其进行优化求解。文中算法采用轮盘赌的方法选择移动行为,并且自适应调整移动策略和行为参数,以克服基本鱼群算法收敛速度较慢和易于陷入局部最优解的不足。
引入居民不满意度概念,基于物理规划思想建立了多地区多目标错峰控制限电模型。模型中不仅考虑到电力分配的经济效益,而且考虑到其社会效益,更加合理可行。在此基础上,进一步根据模糊决策理论给出了相应的多地区多目标错峰控制限电分配策略协调控制策略。
文中对所建立的数学模型和优化控制方法均进行了仿真实验,实验结果表明所提出的各类模型和方法合理可行。
According to the features and development tendency of modern electric system, the following 2 items have become important topics in current electric system research and engineering practice. One is to deeply study the features of modern application technologies for electric system, the other is to develop and perfect the optimized models and practical algorithm for modern electric system. This paper deeply studies several problems existing in the course of modern electric system optimization, and studies its practical algorithm. The main works are as following:
Under the conditions of ensuring acceptability of operation voltage and promoting the voltage stability, in order to minimize the active power loss in power grid, this paper constructs the multiple-purpose reactive power optimization model integrating safety with economical efficiency. This paper offers multiple-purpose particle swarm algorithm aiming at solving the problem of multiple-purpose reactive power optimization. Aiming at the difficult evaluation in solution quality in Pareto optimal set, this paper uses entropy right decision method to optimize and coordinate different dimensions with multiple purposes. Finally, this paper selects the optimal solution.
This thesis offers one kind of improved artificial immune algorithm which takes different mutation strategy toward different unit that has various quality. This algorithm conducts self-adapt adjustment between mutation rate and crossover rate in order to achieve balance between search accuracy and search efficiency. This paper conducts DAIA-BPNN short-term power load forecast model based on DAIA algorithm. It uses DAIA algorithm to optimize the weight and threshold of BPNN while overcoming the blindness when selecting the weight and threshold of BPNN. The actual calculation example of the short-term power system load forecast shows that the method presented in this paper has higher forecast accuracy and robustness compared with artificial neural networks and regression analysis model.
Using the power lines maintenance plan of Liaoning Electric Power Company as background, and according to the chromatic theory of graph theory, this paper constructs the optimized model for power grid maintenance, and introduces the concept of cost-benefit analysis. Further, this paper uses AHP(analytic hierarchy process) and offers one kind of improved fish swarm algorithm in order to get the optimized solution. The algorithm in this paper uses roulette method to select mobile activity, and spontaneously adjusts mobile strategy and activity parameter so as to overcome the shortcomings of basic fish swarm algorithm including slow convergence speed and tendency into local optimal solution.
This thesis introduces the concept of residential unsatisfactory degree. Based on the idea of physical programming, this paper constructs multiple-purpose and multiple-region model for power limiting in peak load shifting control. This model not only covers economical efficiency of electricity distribution, but also covers its social benefit, which makes this model itself more feasible. According to fuzzy decision theory, this paper further offers multiple-purpose and multiple-region coordinating and control strategy for power limiting in peak load shifting control.
The mathematical models and optimized control methods constructed in this paper have all been tested through simulating tests. The result of the tests shows that all the models and methods are reasonable and feasible.
保证运行电压合格率和提高电压稳定性的前提下,为了使电网系统的有功网损最小,文中了建立了集安全性和经济性于一体的多目标无功优化模型。给出了解决多目标无功优化问题的多目标粒子群算法,针对Pareto最优解集中的解质量评价困难的问题,采用熵权决策法进行不同量纲的多目标优化整合,最终选择出满意的最优解。
提出了一种改进的人工免疫算法,算法中对质量不同的个体采用了不同的变异策略,对变异率和交叉率进行了自适应调整,使算法在搜索精度和搜索效率之间达到平衡。构造了基于DAIA算法的DAIA-BPNN电力系统短期负荷预测模型,利用DAIA算法来优化BPNN的权值和阈值,克服了BPNN权值和阈值选择的盲目性。电力系统短期负荷预测的实际算例表明,与人工神经网络及回归分析模型相比,本文所提出的方法具有更高的预测精度和鲁棒性。
以辽宁省电力有限公司线路检修计划为背景,根据图论中的图着色理论建立了电网检修优化模型,引入成本—效益分析概念,利用层次分析法加以分析研究,并提出了种改进的鱼群算法对其进行优化求解。文中算法采用轮盘赌的方法选择移动行为,并且自适应调整移动策略和行为参数,以克服基本鱼群算法收敛速度较慢和易于陷入局部最优解的不足。
引入居民不满意度概念,基于物理规划思想建立了多地区多目标错峰控制限电模型。模型中不仅考虑到电力分配的经济效益,而且考虑到其社会效益,更加合理可行。在此基础上,进一步根据模糊决策理论给出了相应的多地区多目标错峰控制限电分配策略协调控制策略。
文中对所建立的数学模型和优化控制方法均进行了仿真实验,实验结果表明所提出的各类模型和方法合理可行。
According to the features and development tendency of modern electric system, the following 2 items have become important topics in current electric system research and engineering practice. One is to deeply study the features of modern application technologies for electric system, the other is to develop and perfect the optimized models and practical algorithm for modern electric system. This paper deeply studies several problems existing in the course of modern electric system optimization, and studies its practical algorithm. The main works are as following:
Under the conditions of ensuring acceptability of operation voltage and promoting the voltage stability, in order to minimize the active power loss in power grid, this paper constructs the multiple-purpose reactive power optimization model integrating safety with economical efficiency. This paper offers multiple-purpose particle swarm algorithm aiming at solving the problem of multiple-purpose reactive power optimization. Aiming at the difficult evaluation in solution quality in Pareto optimal set, this paper uses entropy right decision method to optimize and coordinate different dimensions with multiple purposes. Finally, this paper selects the optimal solution.
This thesis offers one kind of improved artificial immune algorithm which takes different mutation strategy toward different unit that has various quality. This algorithm conducts self-adapt adjustment between mutation rate and crossover rate in order to achieve balance between search accuracy and search efficiency. This paper conducts DAIA-BPNN short-term power load forecast model based on DAIA algorithm. It uses DAIA algorithm to optimize the weight and threshold of BPNN while overcoming the blindness when selecting the weight and threshold of BPNN. The actual calculation example of the short-term power system load forecast shows that the method presented in this paper has higher forecast accuracy and robustness compared with artificial neural networks and regression analysis model.
Using the power lines maintenance plan of Liaoning Electric Power Company as background, and according to the chromatic theory of graph theory, this paper constructs the optimized model for power grid maintenance, and introduces the concept of cost-benefit analysis. Further, this paper uses AHP(analytic hierarchy process) and offers one kind of improved fish swarm algorithm in order to get the optimized solution. The algorithm in this paper uses roulette method to select mobile activity, and spontaneously adjusts mobile strategy and activity parameter so as to overcome the shortcomings of basic fish swarm algorithm including slow convergence speed and tendency into local optimal solution.
This thesis introduces the concept of residential unsatisfactory degree. Based on the idea of physical programming, this paper constructs multiple-purpose and multiple-region model for power limiting in peak load shifting control. This model not only covers economical efficiency of electricity distribution, but also covers its social benefit, which makes this model itself more feasible. According to fuzzy decision theory, this paper further offers multiple-purpose and multiple-region coordinating and control strategy for power limiting in peak load shifting control.
The mathematical models and optimized control methods constructed in this paper have all been tested through simulating tests. The result of the tests shows that all the models and methods are reasonable and feasible.
引文
1. 侯云鹤.电力系统的群体智能优化及电力市场稳定研究[D],武汉：华中科技大学,2005.
2. 王凌.智能优化算法及其应用[M],北京：清华大学出版社,2001.
3. 丁永生.计算智能：理论、技术与应用[M],北京：科学出版社,2004
4. T Fukuda,D Funato, K Sekiyama, et al. Evaluation on Flexibility of Swarm Intelligent System[C].In:IEEE International Conference on Robotics and Automation 1998.Leuven, Belgium.1998:3210-3215.
5. 张元明,王晓东,李乃湖.基于原对偶内点法的电压无功功率优化[J],电网技术,1998,22(6)：42-45.
6. 王瑞,林飞,游小杰,郑琼林.基于遗传算法的分布式发电系统无功优化控制策略研究[J],电力系统保护与控制,2009,37(2)：24-27.
7. 陈奇,郭瑞鹏.基于改进遗传算法与原对偶内点法的无功优化混合算法[J],电网技术,2008,32(24)：50-54.
8. 黄玮,林知明,李波.基于禁忌搜索粒子群优化算法的无功优化[J],电力学报,2007,4：443-446.
9. 李益华,林文南.一种改进的Tabu Search算法及其在区域电网无功优化中的应用[J],电力科学与技术学,2008,02：60-65.
10.王振树,李林川,李波.基于粒子群与模拟退火相结合的无功优化算法[J],山东大学学报(工学版),2008,8(6)：15-20.
11.吴秀华,吕霞,罗海燕.人工神经网络在电力系统无功电压优化中的应用[J],2008,39(6)：713-717.
12.杨奎河.短期电力负荷的智能化预测方法研究[D],西安：西安电子科技大学,2004.
13.卢芸.短期电力负荷预测关键问题与方法的研究[D],沈阳：沈阳工业大学,2007.
14.邓聚龙.灰预测与灰决策[M].武汉：华中科技大学出版社,2002.9
15. Rahman S, Bhatnagar R. An expert system based algorithm for short-term load forecasting[J].IEEETtrans on Power System,1988,3(2):392-399.
16.崔锦泰(美)著,程正兴译.小波分析导论[M].西安：西安交通大学出版社,1997.
17. Mori H, KobayashinH. Optimal fuzzy inference for short-term load forcasting[J]. IEEE transactions on Power Systems,1996,11(1):39)396.
18.成立奇.关于优化配电网检修计划的研究[D],北京：华北电力大学,2008.
19.缪相林,孙超,李彦,汪芳山,李小明,陈凯.电网检修计划设计的智能分析与可视化实现[J],西安交通大学学报,2005,39(6)：582-585.
20.庞国莉,高焕芝,白灵.面向电网检修计划编排的模拟教学软件的实现[J].福建电脑,2008,9：176.
21.胡之荣,罗曦,胡昌斌.电网电力设备状态检修的决策模型[J].云南电力技术,2008,36：33-34.
22.朴在林,赵斌,刘娜.遗传算法在农村电网检修计划优化中的应用[J],农业工程学报,2007,23(3)：141-145.
23.程丽,缪相林,黄引,张培海.基于黑板体系的电网调度检修票智能决策系统[J],河北工业大学学报,2007,36(2)：42-47.
24.李星梅,乞建勋,杨尚东.基于CC/BM电网设施检修流程优化模型研究与应用[J],电力建设,2006,27(4)：43-46.
25.陈建良,赵永生,张燕平.华中电网发电设备检修计划优化和检修信息管理系统[J],湖北水力发电,2003,4：56-59.
26.雷云川,吕飞鹏,胡美蓉,谢熹,陈冬,刘洋.基于工作流的电网检修计划管理系统研究[J],继电器,2006,34(22)：40-45.
27.葛斐,吴迪,冻实,江山立.电网检修试验限额自动监视软件设计[J],电力系统自动化,2006,30(8)：104-105.
28. Grieco L A, Mascolo S. Performance evaluation and comparison of Westwood[J], New Reno and Vegas TCP congestion control, computer communication Review,2004,34 (2):25-38.
29. Song B, Chung K S, Rhee S H. A new TCP congestion control for high-speed long-distance networks [J]. Information networking lecture notes in Computer Science,2004,30(9):606-615.
30. HJ, LimJ T. On a fair congestion control scheme for TCP [J]. IEEE Communtcations Letters, 2005,9 (2):190-192.
31.莫愿斌.粒子群优化算法的扩展与应用[D],杭州：浙江大学,2006.
32.李凯斌.智能进化优化算法的研究与应用[D],杭州：浙江大学,2008.
33.李澄非.计算智能方法研究及其在流程工业中应用[D],北京：北京化工大学,2007.
34.张利彪.基于粒子群和微分进化的优化算法研究[D],长春：吉林大学,2007.
35.李宁.粒子群优化算法的理论分析与应用研究[D],武汉：华中科技大学,2006.
36.陈泯融.基于极值动力学的优化方法及其应用研究[D],上海：上海交通大学,2008.
37. Kennedy J, Eberhart R C. Particle swarm optimization[C], Proceedings of IEEE International Conference on Neural Networks,1995,1942-1948.
38. Eberhart R C, Kennedy J. A new optimizer using particle swarm theory[C], Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan,1995, 39-43.
39. Eberhart R C, Shi Y H. Particle swarm optimization:Developments, applications and resources[C], Proceedings Congress on Evolutionary Computation 2001, Piscataway, NJ:IEEE Press,2001,81-86.
40. Shi Y, Eberhart R C.A modified particle swarm optimizer[C], IEEE Int. Conf. on Evolutionary Computation, Piscataway, NJ, IEEE Service Center,1998,69-73.
41. Shi Y, Eberhart R C. Empirical study of particle swarm optimization[C], Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, Orlando, FL,2000, 1945-1950.
42. Kennedy J. The particle swarm:social adaptation of knowledge[J], Proc. IEEE Int. Confon. Evolutionary Computation, Indianapolis,1997,303-308.
43.熊盛武,刘麟,王琼等.改进的多目标粒子群算法[J],武汉大学学报,2005,51(3)：308-312.
44.娄素华,吴耀武,熊信银.基于适应度空间距离评估选取的多目标粒子群算法在电网无功优化中的应用[J],电网技术,2007,31(19)：41-46.
45.李宁,邹彤,孙德宝等.基于粒子群的多目标优化算法[J],计算机工程与应用,2005,(23)：43-46.
46. Liu M B, Tso S K, Cheng Y. An extended nonlinear primal-dual interior-point algorithm for feactive-power optimization of large-scale power systems with discrete control variables[J], IEEE Transactions on Power Systems,2002,17(4):982-991.
47. Zhao B, Guo C X, Cao Y J. A multiagent-based particle swarm optimization approach for optimal reactive power dispatch[J], IEEE Transactions on Power Systems,2005,20(2): 1070-1078.
48. Liu Y T, Ma L, Zhang J J. Reactive power optimization by GA/SA/TS combined algorithms[J], International Journal of Electrical Power and Energy Systems,2002,24(9):765-769.
49. Bhagwan D D, Patvardhan C. Reactive power dispatch with a hybrid stochastic search technique[J], International journal of Electrical Power and Energy Systems,2002, 24(9):731-736.
50.熊虎岗,程浩忠,李宏仲.基于免疫算法的多目标无功优化[J],中国电机工程学报,2006,26(11)：102-108.
51.熊虎岗,程浩忠,胡泽春等.基于混沌免疫混合算法的多目标无功优化[J],电网技术,2007,31(11)：33-37.
52.李丹.粒子群优化算法及其应用研究[D],沈阳：东北大学,2008.
53.唐文彬,韩之俊.基于熵值法的财务综合评价方法[J],南京理工大学学报,2001,(12)：65-653.
54. Abido M A, Bakhashwain J M. Optimal VAR dispatch using a multiobjective evolutionary algorithm[J], International Journal of Electrical Power and Energy Systems,2005,27(1):3-20.
55. Wu Q H, Cao Y J, Wen J X. Optimal reactive power dispatch using an adaptive genetic algorithm[J]. Electr power & Energy Syst,1998,20(8):563-569.
56.吴际舜,侯志俭.电力系统潮流计算机方法[M].上海：上海交通大学出版社,2000.
57.刘冰.人工免疫算法及其应用研究[D],重庆：重庆大学,2004.
58.莫宏伟.人工免疫系统原理与应用[M],哈尔滨：哈尔滨工业大学出版社,2002.
59.吕岗.免疫算法及其应用研究[D],徐州：中国矿业大学,2003.
60. Ishida Y, Adachi N. Active Noise Control by an Immune Algorithm [J], Adaptation in Immune System as an Evolution.Proc. ICEC96,1996,150-153.
61. Ishida Y. An Immune Network Model and Its Applications to Process Diagnosis[J],Systemsand Computers in Japan,1993,24(6):646-651.
62. Ishida Y, Mizzessyn F. Learning Algorithms on Immune Network Model[C], Application to Sensor Diagnosis. Proc, IJCNN92, Beijing,1992,33-38.
63. Chun J, Kim M, Jun H. Ahape Optimization of Electromagnetic Devices Using Immune Algorithm [J],IEEE Transactions on Magnetics,1997,33(2):232-240.
64. Chun J, Lim J, Jung H, Yoon J. Optimal Design of Synchronous with Parameter Correction Using Immune Algorithm [J], IEEE Transctions on Energy Conversion,1999(14),3:610-615.
65. Huang S. An Immune-based Optimization Method to Capacitor Placement in a Radial Distribution System [J], IEEE Transaction on Power,2000,3 (15):744-748.
66. Hong J, Lee W, Lee S, Lee B, Lee Y. An Efficient Production Algorithm for MultiHead Surface Mounting Machines Using the Biological Immune Algorithm [J], International Journal of Fuzzy Systems,2000,1 (2):45-53.
67.杨艳云.免疫算法在交流电机矢量控制中的应用研究[D],太原：太原理工大学,2006.
68. Hong J, Lee W, Lee S, Lee B, Lee Y. An Efficient Production Algorithm for MultiHead Surface Mounting Machines Using the Biological Immune Algorithm [J], International Journal of Fuzzy Systems,2000,1 (2):45-53.
69.黄席樾等.现代智能算法理论及应用[M],北京：科学出版社,2005.
70.马佳.改进免疫遗传算法及其在优化调度问题中的应用研究[D],沈阳：东北大学,2008.
71.周志华,曹存根.神经网络及其应用[M],北京：清华大学出版社,2004.
72.孙宁.人工免疫优化算法及其应用研究[D],哈尔滨：哈尔滨工业大学,2006.
73.胡春霞,曾建潮,王清华等.一种免疫微粒群优化算法[J],计算机工程,2007,33(19)：213-214.
74.吴燕玲,卢建刚,孙优贤.基于免疫原理的差分进化[J],控制与决策,2007,22(11)：1309-1312.
75.张迎霞.短期电力负荷预测的神经网络模型优化研究及应用[D],北京：华北电力大学,2006.
76.潘鑫.基于神经网络的短期负荷预测研究[D],南京：河海大学,2004.
77. Soliman S A, Persaud S, Nagar K E, et al. Application of least absolute value parameter estimation based on linear programming to short-term load forecasting[J], Electical Power and Energy Systems,1997,19(3):209-216.
78. Kiartzis S, Kehagias A, Bakirtzis A, et al. Short-term load forecasting using a Bayesian combination method[J], Electr Power Energ Syst,1997,19(3):171-177.
79. Kodogiannis V S, Anagnostakis E M. A study of advanced learning algorithms for short-term load forecasting[J], Engineering Applications of Artificial Intelligence,1999,12(2):159-173.
80. Chen G, Li K, Chung T, et al. Application of an innovative forecasting method in power system load forecasting[J], Electric Power Systems Research,2001,59(2):131-137.
81.周宏,黄婷,戴韧.几种灰色模型用于电力消费中期预测研究[J],电网技术,2000,24(7)：49-54.
82.李伟,韩力.组合灰色预测模型在电力负荷预测中的应用[J],重庆大学学报,2004,27(1)：36-39.
83.张大海,毕研秋,毕研霞.基于串联灰色神经网络的电力负荷预测方法[J],系统工程理论与实践,2004,(12)：128-132.
84. Yao S, SongY, Zhang L. Wavelet transform and neural networks for short-term electrical load forecasting[J], Energy Conversion and Management,2000,41(8):1975-1988.
85. Rocha R A, Alves S A. Multiresolution short-term forecasting for electrical load using wavelet decompositions and neural networks[J], WSEAS Transactions on Systems,2003,2(3):660-665.
86. Zhang B L, Dong Z Y. An adaptive neural-wavelet model for short term load forecasting[J], Electric Power Systems Research,2001,59(2):121-129.
87. Hippert H S, Pedreira C E, Souza R C. Neural Networks for Short-term Load Forecasting:A Review and Evaluation[J], IEEE Trans on power Systems,2001,16(1):44-55.
88.李丹.粒子群优化算法及其应用研究[D],沈阳：东北大学,2008.
89.陈浩.基于人工神经网络的电力短期负荷预测系统研究[D],昆明：昆明理工大学,2005.
90.陈艳.基于遗传神经网络得短期电力负荷预测研究[D],大连：大连理工大学,2005.
91. Hsu C C, Chen C Y. Regional load forecasting in Taiwan:applications of artificial neural networks[J], Energy Convers. Manage.,2003,44(12):1941-1949.
92. SAWA T, FURUKAWA T, NOMOTO M et al. Automatic Scheduling Method Using Tabu Search for Mainenance Outage Tasks of Transmission and Substation System with Network Constraints. In:Proceedings of Power Engineering Society 1999 Winter Meeting, Vol 2. New York(NY, USA):1999.895-900.
93.黄弦超,舒隽,张粒子等.免疫禁忌混合智能优化算法在配电网检修优化中的应用[J].中国电机工程学报,2004,24(11)：96-100.
94.贺鸿祺,周前,王丽等.配电网检修计划制定的实用方法研究[J],试验研究,2006,34(4)：37—41.
95.张粒子,黄弦超,舒隽等.配电网检修计划优化模型设计[J],电力系统自动化,2005, 29(21)：50-52,62.
96.运筹学教材编写组.运筹学[M],北京：清华大学出版社,2005,453-457.
97.李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式：鱼群算法明[J],系统工程理论与实践,2002,22(11)：32-38.
98.李晓磊一种新型的智能优化方法—人工鱼群算法[D].浙江大学博士学位论文,2003.
99. Jeffrey Dean. Animats and what they can tell us [J], Trends in cognitive sciences.1998,2 (1): 60-67.
100.吴斌.车辆路径问题的粒子群算法研究与应用[D],杭州：浙江工业大学,2008.
101.高玉芳.沿海缺水灌区地表水地下水联合调配理论及应用研究[D],南京：河海大学,2007.
102.袁远.改进人工鱼群算法的配电网无功优化[D],南京：南京理工大学,2008.
103.单晓娟.智能计算及其在网络优化中的应用[D],济南：山东大学,2007.
104.王志锐,陈秋园,韩立国,犯为.电力市场环境下基于人工鱼群算法的无功优化研究[J],电力系统,2008,27(10)：72-75.
105.郑华,刘伟,张粒子,杨俊,韩红卫.基于改进人工鱼群算法的电网可用传输能力计算[J],电网技术,2008,32(10)：84-88.
106.张红霞,赵秀明,齐晓娜.基于人工鱼群算法的配电网开关优化配置研究[J].继电器,2007,35(17)：27-30.
107.程晓荣,张秋亮,王智慧,赵惠兰.基于人工鱼群算法的配电网网架优化规划[J].继电器,2007,35(21)：34-38
108.聂宏展,吕盼,乔怡,姚秀萍,姚松.基于人工鱼群算法的输电网络规划[J].电工电能新技术,2008.27(2)：11-15.
109.吴杰,刘健,卢志刚,宋国堂.适用于输电网网架规划的人工鱼群算法[J].电网技术,2007,31(18)：63-67.
110.卢志刚,刘健,吴杰,何艳宾.人工鱼群算法在配电网网架规划中的应用[J].高电压技术,2008,34(3)：565-568.
111. Chuan Li, Shilong Wang. Next-Day Power Market Clearing Price Forecasting Using Artificial Fish-Swarm Based Neural Network[J], Lecture Notes in Computer Science,.2006,3072: 1071-1076.
112.马建伟,张国立.人工鱼群神经网络在电力系统短期负荷预测中的应用[J],电网技术,2005,29(11)：36-39.
113.刘耀年,庞松岭,刘岱.基于人工鱼群算法神经网络的电力系统短期负荷预测[J],电工电能新技术,2005,24(4)：5-8.
114. Lianguo Wang, Yi Hong. A multiagent artificial fish swarm algorithm[C],7th World Congress on Intelligent Control and Automation(2008 WCICA),2008,3161-3166.
115.张梅凤,邵诚.多峰函数优化的生境人工鱼群算法[J].控制理论与应用,2008,25(4)：773-776.
116.黄华娟,周永权.改进型人[鱼群算法及复杂函数全局优化方法[J].广西师范大学学报(自然科学版),2008,26(1)：194-197.
117.王正初.基于人工鱼群算法的复杂系统可靠性优化[J].台州学院学报,2008,30(3)：28-31.
118.聂黎明,周永权.基于人工鱼群算法的机器人路径规划[J].计算机工程与应用,2008,44(32)：48-50.
119.刘白,周永权.基于遗传算法的人工鱼群优化算法[J].计算机工程与设计,2008,29(22)：5827-5829.
120.修春波,张雨虹.基于蚁群与鱼群的混合优化算法[J].计算机工程,2008,34(14)：206-208.
121.殷剑宏,吴开亚.图论及其算法[M].合肥：中国科学技术大学出版社,2003,146-156.
122.张华,王秀坤,孙焘.蚁群算法在考试安排中的应用[J].计算机工程与设计,2003,24(12)：62-64.
123.高立群于宏涛李扬张军正.基于改进蚁群算法的电力线路检修的多目标优化[J],东北大学学报,2007.28(7)：941-944.
124.方英武,孙毅,王轶,冯慧.渭河洪水错峰调度决策支持系统的研究[J].水电自动化与大坝监测,2003,22(6)：66-69.
125.周泰文,王晓星,刘后邗.模糊数学基础简明教程[M],武汉：华中理工大学出版社,1993.
126.唐焕文,秦学志.最优化方法[M],大连：大连理工大学出版社,1994.
127. Chiclana F, Herrera F, Herrera Viedma E, Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations [J]. Fuzzy Sets and Systems, 1998,97:33-48.
128. Puerto J, Marmol A M, Monroy L, Fernandez F R. Decision criteria with partial information [J]. Intl. Trans. In Op. Res.2000(7):51-65.
129. Eiji Takeda. A method for multiple pseudo-criteria decision problems [J]. Computers&Operations Research,2001 (28):1427-1439.
130. Xu X Z, Martel J M, Lamond B F. A multiple criteria ranking procedure based on distance between partial preorders [J]. European Journal of Operational Research,2001,133:69-80.
131. Eum Y. S., Park K. S., Kim S. H. Establishing dominance and potential optimality in multi-criteria analysis with imprecise weight and value [J]. Computer and Operations research, 2001,28:397-409.
132. Lee K S, Park K S, Eum Y S, Park K. Extended methods for identifying dominance and potential optimality in multi-criteria analysis with imprecise information [J]. European Journal of Operational Research,2001,134:557-563.
133. Puerto J, Marmol A M, Monroy L, Fernandez F R. Decision criteria with partial information [J]. Intl. Trans. In.Op. Res.2000(7):51-65.
134. Kim S H, Choi S H, Ahn B S. Interactive group decision process with evolutionary database [J]. Decision Support System,1998,23:333-345.
135. Kim J K, Choi S H. A utility range-based information group support system for mufti-attribute decision-making [J]. Computers&Operations Research,2001,28:485-505.
136. Kim S H, Han C H. An interactive procedure for mufti-attribute group decision making with incomplete information [J]. Computers&Operations Research,1999,26:755-772.
137. Dias L. ELECTRE TRI for groups imprecise information on Parameter values [J]. Group decision and Negotiation,2000,9,355-377.
138. Marmol A M, Puerto J, Fernandez F R. The use of partial information on weights in mufti-criteria decision problems [J]. Journal of mufti-criteria decision analysis,1998,7:322-329.
139. Marmol A M, Puerto J, Fernandez F R. Sequential incorporation of imprecise information in multiple criteria decision process [J].European Journal of Operational Research,2002, 137:123-133.
140.陈挺,决策分析[M],北京：科学出版社[M],1987.
141.魏世孝,周献中.多属性决策理论方法及其在C'I系统中的应用[M],北京：国防工业出版社,1998.
142.杨自厚,李宝泽.多指标决策理论与方法[M],沈阳：东北工学院出版社,1989.
143.宣家琪.多目标决策[M],长沙：湖南科技出版社,1989.
144.徐南荣,仲伟俊.科学决策理论与方法[M],南京：东南大学出版社,1996.
145.宋庆克,汪希龄,胡铁牛.多属性评价方法及发展评述[J].决策与决策支持系统,1997,7(4)：128-137.
146.苏波,王烷尘.群决策研究的评述[J],决策与决策支持系统,1995,1(3)：115-124.
147.刘树林,邱莞华.多属性决策基础理论研究[J],系统工程理论与实践,1998,18(1)：38-42.
148.章志敏.多属性决策方法及应用[J],系统工程理论与实践,1994,14(10)：8-10.
149. Ben Khelifa S, Martel J M.A distance-Based Collective Weak Ordering [J]. Group Decision and Negotiation,2001,10:317-329.
150. Yang Kyoon Lee, Kyung Sam Park, Soung Hie Kim. Identification of inefficiencies in an additive model bussed IDEA (imprecise date envelopment analysis) [J]. Computers&Operations,2002, 29:1661-1676.
151. Salo A A, Hamalainen R P. Preference ratios in mufti-attribute evaluation (PRIME)-Elicitation and Decision Procedures under incomplete information [J]. IEEE Transactions on Systems, Man, Cybernetics,2001,31 (6):533-545.
152.姚新胜,黄洪钟,周仲荣.机械满意度优化中满意函数的建立方法[J],机械科学与技术,2004,(23)4：399-401.
153.王允良,李为吉.物理规划方法及其在飞机方案设计中的应用[J],航空学报,2005,26(5)：562-565.
154.袁旭,田志刚.模糊物理规划及其应用研究[J],广西大学学报,2004,29(2)：105-108.
155.黄洪钟,刘鸿莉,古莹奎等.基于物理规划的模糊稳健优化设计[J],清华大学学报,2005,45(8)：1020-1022.
156. Messac. A. Physical programming:Effective Optimization for Computational Design[J], AIAA Journal,1996,34(1):149-158.
157.李扬.电力系统中错峰控制与牛鞭效应的研究[D],沈阳：东北大学,2008.
2. 王凌.智能优化算法及其应用[M],北京：清华大学出版社,2001.
3. 丁永生.计算智能：理论、技术与应用[M],北京：科学出版社,2004
4. T Fukuda,D Funato, K Sekiyama, et al. Evaluation on Flexibility of Swarm Intelligent System[C].In:IEEE International Conference on Robotics and Automation 1998.Leuven, Belgium.1998:3210-3215.
5. 张元明,王晓东,李乃湖.基于原对偶内点法的电压无功功率优化[J],电网技术,1998,22(6)：42-45.
6. 王瑞,林飞,游小杰,郑琼林.基于遗传算法的分布式发电系统无功优化控制策略研究[J],电力系统保护与控制,2009,37(2)：24-27.
7. 陈奇,郭瑞鹏.基于改进遗传算法与原对偶内点法的无功优化混合算法[J],电网技术,2008,32(24)：50-54.
8. 黄玮,林知明,李波.基于禁忌搜索粒子群优化算法的无功优化[J],电力学报,2007,4：443-446.
9. 李益华,林文南.一种改进的Tabu Search算法及其在区域电网无功优化中的应用[J],电力科学与技术学,2008,02：60-65.
10.王振树,李林川,李波.基于粒子群与模拟退火相结合的无功优化算法[J],山东大学学报(工学版),2008,8(6)：15-20.
11.吴秀华,吕霞,罗海燕.人工神经网络在电力系统无功电压优化中的应用[J],2008,39(6)：713-717.
12.杨奎河.短期电力负荷的智能化预测方法研究[D],西安：西安电子科技大学,2004.
13.卢芸.短期电力负荷预测关键问题与方法的研究[D],沈阳：沈阳工业大学,2007.
14.邓聚龙.灰预测与灰决策[M].武汉：华中科技大学出版社,2002.9
15. Rahman S, Bhatnagar R. An expert system based algorithm for short-term load forecasting[J].IEEETtrans on Power System,1988,3(2):392-399.
16.崔锦泰(美)著,程正兴译.小波分析导论[M].西安：西安交通大学出版社,1997.
17. Mori H, KobayashinH. Optimal fuzzy inference for short-term load forcasting[J]. IEEE transactions on Power Systems,1996,11(1):39)396.
18.成立奇.关于优化配电网检修计划的研究[D],北京：华北电力大学,2008.
19.缪相林,孙超,李彦,汪芳山,李小明,陈凯.电网检修计划设计的智能分析与可视化实现[J],西安交通大学学报,2005,39(6)：582-585.
20.庞国莉,高焕芝,白灵.面向电网检修计划编排的模拟教学软件的实现[J].福建电脑,2008,9：176.
21.胡之荣,罗曦,胡昌斌.电网电力设备状态检修的决策模型[J].云南电力技术,2008,36：33-34.
22.朴在林,赵斌,刘娜.遗传算法在农村电网检修计划优化中的应用[J],农业工程学报,2007,23(3)：141-145.
23.程丽,缪相林,黄引,张培海.基于黑板体系的电网调度检修票智能决策系统[J],河北工业大学学报,2007,36(2)：42-47.
24.李星梅,乞建勋,杨尚东.基于CC/BM电网设施检修流程优化模型研究与应用[J],电力建设,2006,27(4)：43-46.
25.陈建良,赵永生,张燕平.华中电网发电设备检修计划优化和检修信息管理系统[J],湖北水力发电,2003,4：56-59.
26.雷云川,吕飞鹏,胡美蓉,谢熹,陈冬,刘洋.基于工作流的电网检修计划管理系统研究[J],继电器,2006,34(22)：40-45.
27.葛斐,吴迪,冻实,江山立.电网检修试验限额自动监视软件设计[J],电力系统自动化,2006,30(8)：104-105.
28. Grieco L A, Mascolo S. Performance evaluation and comparison of Westwood[J], New Reno and Vegas TCP congestion control, computer communication Review,2004,34 (2):25-38.
29. Song B, Chung K S, Rhee S H. A new TCP congestion control for high-speed long-distance networks [J]. Information networking lecture notes in Computer Science,2004,30(9):606-615.
30. HJ, LimJ T. On a fair congestion control scheme for TCP [J]. IEEE Communtcations Letters, 2005,9 (2):190-192.
31.莫愿斌.粒子群优化算法的扩展与应用[D],杭州：浙江大学,2006.
32.李凯斌.智能进化优化算法的研究与应用[D],杭州：浙江大学,2008.
33.李澄非.计算智能方法研究及其在流程工业中应用[D],北京：北京化工大学,2007.
34.张利彪.基于粒子群和微分进化的优化算法研究[D],长春：吉林大学,2007.
35.李宁.粒子群优化算法的理论分析与应用研究[D],武汉：华中科技大学,2006.
36.陈泯融.基于极值动力学的优化方法及其应用研究[D],上海：上海交通大学,2008.
37. Kennedy J, Eberhart R C. Particle swarm optimization[C], Proceedings of IEEE International Conference on Neural Networks,1995,1942-1948.
38. Eberhart R C, Kennedy J. A new optimizer using particle swarm theory[C], Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan,1995, 39-43.
39. Eberhart R C, Shi Y H. Particle swarm optimization:Developments, applications and resources[C], Proceedings Congress on Evolutionary Computation 2001, Piscataway, NJ:IEEE Press,2001,81-86.
40. Shi Y, Eberhart R C.A modified particle swarm optimizer[C], IEEE Int. Conf. on Evolutionary Computation, Piscataway, NJ, IEEE Service Center,1998,69-73.
41. Shi Y, Eberhart R C. Empirical study of particle swarm optimization[C], Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, Orlando, FL,2000, 1945-1950.
42. Kennedy J. The particle swarm:social adaptation of knowledge[J], Proc. IEEE Int. Confon. Evolutionary Computation, Indianapolis,1997,303-308.
43.熊盛武,刘麟,王琼等.改进的多目标粒子群算法[J],武汉大学学报,2005,51(3)：308-312.
44.娄素华,吴耀武,熊信银.基于适应度空间距离评估选取的多目标粒子群算法在电网无功优化中的应用[J],电网技术,2007,31(19)：41-46.
45.李宁,邹彤,孙德宝等.基于粒子群的多目标优化算法[J],计算机工程与应用,2005,(23)：43-46.
46. Liu M B, Tso S K, Cheng Y. An extended nonlinear primal-dual interior-point algorithm for feactive-power optimization of large-scale power systems with discrete control variables[J], IEEE Transactions on Power Systems,2002,17(4):982-991.
47. Zhao B, Guo C X, Cao Y J. A multiagent-based particle swarm optimization approach for optimal reactive power dispatch[J], IEEE Transactions on Power Systems,2005,20(2): 1070-1078.
48. Liu Y T, Ma L, Zhang J J. Reactive power optimization by GA/SA/TS combined algorithms[J], International Journal of Electrical Power and Energy Systems,2002,24(9):765-769.
49. Bhagwan D D, Patvardhan C. Reactive power dispatch with a hybrid stochastic search technique[J], International journal of Electrical Power and Energy Systems,2002, 24(9):731-736.
50.熊虎岗,程浩忠,李宏仲.基于免疫算法的多目标无功优化[J],中国电机工程学报,2006,26(11)：102-108.
51.熊虎岗,程浩忠,胡泽春等.基于混沌免疫混合算法的多目标无功优化[J],电网技术,2007,31(11)：33-37.
52.李丹.粒子群优化算法及其应用研究[D],沈阳：东北大学,2008.
53.唐文彬,韩之俊.基于熵值法的财务综合评价方法[J],南京理工大学学报,2001,(12)：65-653.
54. Abido M A, Bakhashwain J M. Optimal VAR dispatch using a multiobjective evolutionary algorithm[J], International Journal of Electrical Power and Energy Systems,2005,27(1):3-20.
55. Wu Q H, Cao Y J, Wen J X. Optimal reactive power dispatch using an adaptive genetic algorithm[J]. Electr power & Energy Syst,1998,20(8):563-569.
56.吴际舜,侯志俭.电力系统潮流计算机方法[M].上海：上海交通大学出版社,2000.
57.刘冰.人工免疫算法及其应用研究[D],重庆：重庆大学,2004.
58.莫宏伟.人工免疫系统原理与应用[M],哈尔滨：哈尔滨工业大学出版社,2002.
59.吕岗.免疫算法及其应用研究[D],徐州：中国矿业大学,2003.
60. Ishida Y, Adachi N. Active Noise Control by an Immune Algorithm [J], Adaptation in Immune System as an Evolution.Proc. ICEC96,1996,150-153.
61. Ishida Y. An Immune Network Model and Its Applications to Process Diagnosis[J],Systemsand Computers in Japan,1993,24(6):646-651.
62. Ishida Y, Mizzessyn F. Learning Algorithms on Immune Network Model[C], Application to Sensor Diagnosis. Proc, IJCNN92, Beijing,1992,33-38.
63. Chun J, Kim M, Jun H. Ahape Optimization of Electromagnetic Devices Using Immune Algorithm [J],IEEE Transactions on Magnetics,1997,33(2):232-240.
64. Chun J, Lim J, Jung H, Yoon J. Optimal Design of Synchronous with Parameter Correction Using Immune Algorithm [J], IEEE Transctions on Energy Conversion,1999(14),3:610-615.
65. Huang S. An Immune-based Optimization Method to Capacitor Placement in a Radial Distribution System [J], IEEE Transaction on Power,2000,3 (15):744-748.
66. Hong J, Lee W, Lee S, Lee B, Lee Y. An Efficient Production Algorithm for MultiHead Surface Mounting Machines Using the Biological Immune Algorithm [J], International Journal of Fuzzy Systems,2000,1 (2):45-53.
67.杨艳云.免疫算法在交流电机矢量控制中的应用研究[D],太原：太原理工大学,2006.
68. Hong J, Lee W, Lee S, Lee B, Lee Y. An Efficient Production Algorithm for MultiHead Surface Mounting Machines Using the Biological Immune Algorithm [J], International Journal of Fuzzy Systems,2000,1 (2):45-53.
69.黄席樾等.现代智能算法理论及应用[M],北京：科学出版社,2005.
70.马佳.改进免疫遗传算法及其在优化调度问题中的应用研究[D],沈阳：东北大学,2008.
71.周志华,曹存根.神经网络及其应用[M],北京：清华大学出版社,2004.
72.孙宁.人工免疫优化算法及其应用研究[D],哈尔滨：哈尔滨工业大学,2006.
73.胡春霞,曾建潮,王清华等.一种免疫微粒群优化算法[J],计算机工程,2007,33(19)：213-214.
74.吴燕玲,卢建刚,孙优贤.基于免疫原理的差分进化[J],控制与决策,2007,22(11)：1309-1312.
75.张迎霞.短期电力负荷预测的神经网络模型优化研究及应用[D],北京：华北电力大学,2006.
76.潘鑫.基于神经网络的短期负荷预测研究[D],南京：河海大学,2004.
77. Soliman S A, Persaud S, Nagar K E, et al. Application of least absolute value parameter estimation based on linear programming to short-term load forecasting[J], Electical Power and Energy Systems,1997,19(3):209-216.
78. Kiartzis S, Kehagias A, Bakirtzis A, et al. Short-term load forecasting using a Bayesian combination method[J], Electr Power Energ Syst,1997,19(3):171-177.
79. Kodogiannis V S, Anagnostakis E M. A study of advanced learning algorithms for short-term load forecasting[J], Engineering Applications of Artificial Intelligence,1999,12(2):159-173.
80. Chen G, Li K, Chung T, et al. Application of an innovative forecasting method in power system load forecasting[J], Electric Power Systems Research,2001,59(2):131-137.
81.周宏,黄婷,戴韧.几种灰色模型用于电力消费中期预测研究[J],电网技术,2000,24(7)：49-54.
82.李伟,韩力.组合灰色预测模型在电力负荷预测中的应用[J],重庆大学学报,2004,27(1)：36-39.
83.张大海,毕研秋,毕研霞.基于串联灰色神经网络的电力负荷预测方法[J],系统工程理论与实践,2004,(12)：128-132.
84. Yao S, SongY, Zhang L. Wavelet transform and neural networks for short-term electrical load forecasting[J], Energy Conversion and Management,2000,41(8):1975-1988.
85. Rocha R A, Alves S A. Multiresolution short-term forecasting for electrical load using wavelet decompositions and neural networks[J], WSEAS Transactions on Systems,2003,2(3):660-665.
86. Zhang B L, Dong Z Y. An adaptive neural-wavelet model for short term load forecasting[J], Electric Power Systems Research,2001,59(2):121-129.
87. Hippert H S, Pedreira C E, Souza R C. Neural Networks for Short-term Load Forecasting:A Review and Evaluation[J], IEEE Trans on power Systems,2001,16(1):44-55.
88.李丹.粒子群优化算法及其应用研究[D],沈阳：东北大学,2008.
89.陈浩.基于人工神经网络的电力短期负荷预测系统研究[D],昆明：昆明理工大学,2005.
90.陈艳.基于遗传神经网络得短期电力负荷预测研究[D],大连：大连理工大学,2005.
91. Hsu C C, Chen C Y. Regional load forecasting in Taiwan:applications of artificial neural networks[J], Energy Convers. Manage.,2003,44(12):1941-1949.
92. SAWA T, FURUKAWA T, NOMOTO M et al. Automatic Scheduling Method Using Tabu Search for Mainenance Outage Tasks of Transmission and Substation System with Network Constraints. In:Proceedings of Power Engineering Society 1999 Winter Meeting, Vol 2. New York(NY, USA):1999.895-900.
93.黄弦超,舒隽,张粒子等.免疫禁忌混合智能优化算法在配电网检修优化中的应用[J].中国电机工程学报,2004,24(11)：96-100.
94.贺鸿祺,周前,王丽等.配电网检修计划制定的实用方法研究[J],试验研究,2006,34(4)：37—41.
95.张粒子,黄弦超,舒隽等.配电网检修计划优化模型设计[J],电力系统自动化,2005, 29(21)：50-52,62.
96.运筹学教材编写组.运筹学[M],北京：清华大学出版社,2005,453-457.
97.李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式：鱼群算法明[J],系统工程理论与实践,2002,22(11)：32-38.
98.李晓磊一种新型的智能优化方法—人工鱼群算法[D].浙江大学博士学位论文,2003.
99. Jeffrey Dean. Animats and what they can tell us [J], Trends in cognitive sciences.1998,2 (1): 60-67.
100.吴斌.车辆路径问题的粒子群算法研究与应用[D],杭州：浙江工业大学,2008.
101.高玉芳.沿海缺水灌区地表水地下水联合调配理论及应用研究[D],南京：河海大学,2007.
102.袁远.改进人工鱼群算法的配电网无功优化[D],南京：南京理工大学,2008.
103.单晓娟.智能计算及其在网络优化中的应用[D],济南：山东大学,2007.
104.王志锐,陈秋园,韩立国,犯为.电力市场环境下基于人工鱼群算法的无功优化研究[J],电力系统,2008,27(10)：72-75.
105.郑华,刘伟,张粒子,杨俊,韩红卫.基于改进人工鱼群算法的电网可用传输能力计算[J],电网技术,2008,32(10)：84-88.
106.张红霞,赵秀明,齐晓娜.基于人工鱼群算法的配电网开关优化配置研究[J].继电器,2007,35(17)：27-30.
107.程晓荣,张秋亮,王智慧,赵惠兰.基于人工鱼群算法的配电网网架优化规划[J].继电器,2007,35(21)：34-38
108.聂宏展,吕盼,乔怡,姚秀萍,姚松.基于人工鱼群算法的输电网络规划[J].电工电能新技术,2008.27(2)：11-15.
109.吴杰,刘健,卢志刚,宋国堂.适用于输电网网架规划的人工鱼群算法[J].电网技术,2007,31(18)：63-67.
110.卢志刚,刘健,吴杰,何艳宾.人工鱼群算法在配电网网架规划中的应用[J].高电压技术,2008,34(3)：565-568.
111. Chuan Li, Shilong Wang. Next-Day Power Market Clearing Price Forecasting Using Artificial Fish-Swarm Based Neural Network[J], Lecture Notes in Computer Science,.2006,3072: 1071-1076.
112.马建伟,张国立.人工鱼群神经网络在电力系统短期负荷预测中的应用[J],电网技术,2005,29(11)：36-39.
113.刘耀年,庞松岭,刘岱.基于人工鱼群算法神经网络的电力系统短期负荷预测[J],电工电能新技术,2005,24(4)：5-8.
114. Lianguo Wang, Yi Hong. A multiagent artificial fish swarm algorithm[C],7th World Congress on Intelligent Control and Automation(2008 WCICA),2008,3161-3166.
115.张梅凤,邵诚.多峰函数优化的生境人工鱼群算法[J].控制理论与应用,2008,25(4)：773-776.
116.黄华娟,周永权.改进型人[鱼群算法及复杂函数全局优化方法[J].广西师范大学学报(自然科学版),2008,26(1)：194-197.
117.王正初.基于人工鱼群算法的复杂系统可靠性优化[J].台州学院学报,2008,30(3)：28-31.
118.聂黎明,周永权.基于人工鱼群算法的机器人路径规划[J].计算机工程与应用,2008,44(32)：48-50.
119.刘白,周永权.基于遗传算法的人工鱼群优化算法[J].计算机工程与设计,2008,29(22)：5827-5829.
120.修春波,张雨虹.基于蚁群与鱼群的混合优化算法[J].计算机工程,2008,34(14)：206-208.
121.殷剑宏,吴开亚.图论及其算法[M].合肥：中国科学技术大学出版社,2003,146-156.
122.张华,王秀坤,孙焘.蚁群算法在考试安排中的应用[J].计算机工程与设计,2003,24(12)：62-64.
123.高立群于宏涛李扬张军正.基于改进蚁群算法的电力线路检修的多目标优化[J],东北大学学报,2007.28(7)：941-944.
124.方英武,孙毅,王轶,冯慧.渭河洪水错峰调度决策支持系统的研究[J].水电自动化与大坝监测,2003,22(6)：66-69.
125.周泰文,王晓星,刘后邗.模糊数学基础简明教程[M],武汉：华中理工大学出版社,1993.
126.唐焕文,秦学志.最优化方法[M],大连：大连理工大学出版社,1994.
127. Chiclana F, Herrera F, Herrera Viedma E, Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations [J]. Fuzzy Sets and Systems, 1998,97:33-48.
128. Puerto J, Marmol A M, Monroy L, Fernandez F R. Decision criteria with partial information [J]. Intl. Trans. In Op. Res.2000(7):51-65.
129. Eiji Takeda. A method for multiple pseudo-criteria decision problems [J]. Computers&Operations Research,2001 (28):1427-1439.
130. Xu X Z, Martel J M, Lamond B F. A multiple criteria ranking procedure based on distance between partial preorders [J]. European Journal of Operational Research,2001,133:69-80.
131. Eum Y. S., Park K. S., Kim S. H. Establishing dominance and potential optimality in multi-criteria analysis with imprecise weight and value [J]. Computer and Operations research, 2001,28:397-409.
132. Lee K S, Park K S, Eum Y S, Park K. Extended methods for identifying dominance and potential optimality in multi-criteria analysis with imprecise information [J]. European Journal of Operational Research,2001,134:557-563.
133. Puerto J, Marmol A M, Monroy L, Fernandez F R. Decision criteria with partial information [J]. Intl. Trans. In.Op. Res.2000(7):51-65.
134. Kim S H, Choi S H, Ahn B S. Interactive group decision process with evolutionary database [J]. Decision Support System,1998,23:333-345.
135. Kim J K, Choi S H. A utility range-based information group support system for mufti-attribute decision-making [J]. Computers&Operations Research,2001,28:485-505.
136. Kim S H, Han C H. An interactive procedure for mufti-attribute group decision making with incomplete information [J]. Computers&Operations Research,1999,26:755-772.
137. Dias L. ELECTRE TRI for groups imprecise information on Parameter values [J]. Group decision and Negotiation,2000,9,355-377.
138. Marmol A M, Puerto J, Fernandez F R. The use of partial information on weights in mufti-criteria decision problems [J]. Journal of mufti-criteria decision analysis,1998,7:322-329.
139. Marmol A M, Puerto J, Fernandez F R. Sequential incorporation of imprecise information in multiple criteria decision process [J].European Journal of Operational Research,2002, 137:123-133.
140.陈挺,决策分析[M],北京：科学出版社[M],1987.
141.魏世孝,周献中.多属性决策理论方法及其在C'I系统中的应用[M],北京：国防工业出版社,1998.
142.杨自厚,李宝泽.多指标决策理论与方法[M],沈阳：东北工学院出版社,1989.
143.宣家琪.多目标决策[M],长沙：湖南科技出版社,1989.
144.徐南荣,仲伟俊.科学决策理论与方法[M],南京：东南大学出版社,1996.
145.宋庆克,汪希龄,胡铁牛.多属性评价方法及发展评述[J].决策与决策支持系统,1997,7(4)：128-137.
146.苏波,王烷尘.群决策研究的评述[J],决策与决策支持系统,1995,1(3)：115-124.
147.刘树林,邱莞华.多属性决策基础理论研究[J],系统工程理论与实践,1998,18(1)：38-42.
148.章志敏.多属性决策方法及应用[J],系统工程理论与实践,1994,14(10)：8-10.
149. Ben Khelifa S, Martel J M.A distance-Based Collective Weak Ordering [J]. Group Decision and Negotiation,2001,10:317-329.
150. Yang Kyoon Lee, Kyung Sam Park, Soung Hie Kim. Identification of inefficiencies in an additive model bussed IDEA (imprecise date envelopment analysis) [J]. Computers&Operations,2002, 29:1661-1676.
151. Salo A A, Hamalainen R P. Preference ratios in mufti-attribute evaluation (PRIME)-Elicitation and Decision Procedures under incomplete information [J]. IEEE Transactions on Systems, Man, Cybernetics,2001,31 (6):533-545.
152.姚新胜,黄洪钟,周仲荣.机械满意度优化中满意函数的建立方法[J],机械科学与技术,2004,(23)4：399-401.
153.王允良,李为吉.物理规划方法及其在飞机方案设计中的应用[J],航空学报,2005,26(5)：562-565.
154.袁旭,田志刚.模糊物理规划及其应用研究[J],广西大学学报,2004,29(2)：105-108.
155.黄洪钟,刘鸿莉,古莹奎等.基于物理规划的模糊稳健优化设计[J],清华大学学报,2005,45(8)：1020-1022.
156. Messac. A. Physical programming:Effective Optimization for Computational Design[J], AIAA Journal,1996,34(1):149-158.
157.李扬.电力系统中错峰控制与牛鞭效应的研究[D],沈阳：东北大学,2008.