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计算实验研究方法及应用

崔凯楠 郑晓龙 文丁 赵学亮

崔凯楠, 郑晓龙, 文丁, 赵学亮. 计算实验研究方法及应用. 自动化学报, 2013, 39(8): 1157-1169. doi: 10.3724/SP.J.1004.2013.01157
引用本文: 崔凯楠, 郑晓龙, 文丁, 赵学亮. 计算实验研究方法及应用. 自动化学报, 2013, 39(8): 1157-1169. doi: 10.3724/SP.J.1004.2013.01157
CUI Kai-Nan, ZHENG Xiao-Long, WEN Ding, ZHAO Xue-Liang. Researches and Applications of Computational Experiments. ACTA AUTOMATICA SINICA, 2013, 39(8): 1157-1169. doi: 10.3724/SP.J.1004.2013.01157
Citation: CUI Kai-Nan, ZHENG Xiao-Long, WEN Ding, ZHAO Xue-Liang. Researches and Applications of Computational Experiments. ACTA AUTOMATICA SINICA, 2013, 39(8): 1157-1169. doi: 10.3724/SP.J.1004.2013.01157

计算实验研究方法及应用

doi: 10.3724/SP.J.1004.2013.01157
基金项目: 

国家自然科学基金(71103180, 91124001, 71025001, 91024030);国家科技重大专项基金(2012ZX10004801, 2013ZX10004218)资助

详细信息
    作者简介:

    崔凯楠 西安交通大学电信学院博士研究生. 主要研究方向为计算实验与社会媒体分析. E-mail: kainan.cui@live.cn

Researches and Applications of Computational Experiments

Funds: 

Supported by National Natural Science Foundation of China (71103180, 91124001, 71025001, 91024030), and National Science and Technology Major Project (2012ZX10004801, 2013ZX1 0004218)

  • 摘要: 计算实验是一种研究复杂系统的新兴计算方法,受到了国内外学者的广泛关注.近年来,随着相关研究的不断发展, 计算实验方法在多个领域显示出巨大的应用前景,特别是复杂系统管理与控制相关的诸多重要领域,如社会安全、电子商务、金融市场等. 本文将首先介绍计算实验的主要思想以及实验设计与计算机仿真等计算实验的研究基础;其次, 我们将介绍计算实验的主要研究内容,包括计 算模型构建、计算实验设计以及计算实验执行; 最后,我们将讨论计算实验的应用情况,总结研究过程中面临的挑战并介绍潜在的研究方向.
  • [1] Wang Fei-Yue. Artificial societies, computational experiments, and parallel systems: a discussion on computational theory of complex social-economic systems. Complex Systems and Complexity Science, 2004, 1(4): 25-35 (王飞跃. 人工社会、计算实验、平行系统关于复杂社会经济系统计算研究的讨论. 复杂系统与复杂性科学, 2004, 1(4): 25-35)
    [2] Wang Fei-Yue. Computational experiments for behavior analysis and decision evaluation of complex systems. Journal of System Simulation, 2004, 16(5): 893-897 (王飞跃. 计算实验方法与复杂系统行为分析和决策评估. 系统仿真学报, 2004, 16(5): 893-897)
    [3] Wang Fei-Yue. Computational theory and method on complex system. China Basic Science, 2004, 6(5): 3-10(王飞跃. 关于复杂系统研究的计算理论与方法. 中国基础科学, 2004, 6(5): 3-10)
    [4] [4] Montgomery D C. Design and Analysis of Experiments. New York: John Wiley and Sons Inc., 2008
    [5] [5] Fisher R A. The Design of Experiments. New York: Macmillan Pub. Co., 1971
    [6] [6] Box G E P, Wilson K B. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society. Series B (Methodological), 1951, 13(1): 1-45
    [7] [7] Box G E P. Statistics as a catalyst to learning by scientific method, Part IIA discussion. Journal of Quality Technology, 1999, 31(1): 16-29
    [8] [8] Taguchi G, Wu Y. Introduction to Off-line Quality Control. Nagoya: Central Japan Quality Control Assoc, 1979
    [9] [9] Kackar R N. Off-line quality control, parameter design, and the Taguchi method. Journal of Quality Technology, 1985, 17: 176-188
    [10] Taguchi G. Introduction to Quality Engineering: Designing Quality into Products and Processes. Quality Resources, 1986
    [11] Wolf A, Henes D, Bogdanski S, Lutz T, Krmer E. Statistical analysis of parameter variations using the Taguchi method. In: Proceedings of the 2013 Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics. Berlin, Heidelberg: Springer, 2013. 247-264
    [12] Law A M, Kelton D W. Simulation Modeling and Analysis. New York: McGraw-Hill, 2000
    [13] Fishwick P A. Simulation Model Design and Execution: Building Digital Worlds. Upper Saddle River, NJ: Prentice Hall, 1995
    [14] Rubinstein R Y, Kroese D P. Simulation and the Monte Carlo Method. New York: Wiley-Interscience, 2011
    [15] Mielczarek B, Uzia ko-Mydlikowska J. Application of computer simulation modeling in the health care sector: a survey. SIMULATION, 2012, 88(2): 197-216
    [16] Teichroew D, Lubin J F. Computer simulation-discussion of the technique and comparison of Languages. Communications of the ACM, 1966, 9(10): 723-741
    [17] Balci O. A life cycle for modeling and simulation. Simulation, 2012, 88(7): 870-883
    [18] Fujimoto R M. Parallel and Distributed Simulation Systems. New York: Wiley, 2000
    [19] Naylor T H. Computer Simulation Techniques. New York: Wiley, 1966
    [20] Kleijnen J P C. Experimental design for sensitivity analysis, optimization, and validation of simulation models. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice. Hoboken, NJ: John Wiley Sons, 2007. 173-223
    [21] Sargent R G. Verification and validation of simulation models. In: Proceedings of the 37th Conference on Winter Simulation. Orlando, Florida: Winter Simulation Conference, 2005. 130-143
    [22] Kleijnen J P C. Verification and validation of simulation models. European Journal of Operational Research, 1995, 82(1): 145-162
    [23] Fagiolo G, Moneta A, Windrum P. A critical guide to empirical validation of agent-based models in economics: methodologies, procedures, and open problems. Computational Economics, 2007, 30(3): 195-226
    [24] Burdick D S, Naylor T H. Design of computer simulation experiments for industrial systems. Communications of the ACM, 1966, 9(5): 329-339
    [25] Lee L H, Chew E P, Frazier P I, Jia Q S, Chen C H. Advances in simulation optimization and its applications. IIE Transactions, 2013, 45(7): 683-684
    [26] Carson Y, Maria A. Simulation optimization: methods and applications. In: Proceedings of the 29th Conference on Winter Simulation. Atlanta, Georgia: Winter Simulation Conference, 1997. 118-126
    [27] Fu M C. Optimization via simulation: a review. Annals of Operations Research, 1994, 53(1): 199-247
    [28] Swisher J R, Hyden P D, Jacobson S H, Schruben L W. A survey of recent advances in discrete input parameter discrete-event simulation optimization. IIE Transactions, 2004, 36(6): 591-600
    [29] Swisher J R, Jacobson S H, Ycesan E. Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey. ACM Transactions on Modeling and Computer Simulation, 2003, 13(2): 134-154
    [30] Wang Fei-Yue. On the modeling, analysis, control and management of complex systems. Complex Systems and Complexity Science, 2006, 3(2): 26-34 (王飞跃. 关于复杂系统的建模、分析、控制和管理. 复杂系统与复杂性科学, 2006, 3(2): 26-34)
    [31] Kohler T A, Gumerman G G. Dynamics in Human and Primate Societies: Agent-based Modeling of Social and Spatial Processes. Oxford: Oxford University Press, 2000.
    [32] Lansing J S. Artificial societies and the social sciences. Artificial Life, 2002, 8(3): 279-292
    [33] Prietula M, Carley K, Gasser L. Simulating Organizations: Computational Models of Institutions and Groups. Cambridge: AAAI Press/MIT Press, 1998
    [34] Wang Fei-Yue, Lansing J-Stephen. From artificial life to artificial societiesnew methods for studies of complex social systems. Complex Systems and Complexity Science, 2004, 1(1): 33-41(王飞跃, 兰森史帝夫. 从人工生命到人工社会复杂社会系统研究的现状和展望. 复杂系统与复杂性科学, 2004, 1(1): 33-41)
    [35] Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5): 485-489, 541 (王飞跃. 平行系统方法与复杂系统的管理和控制. 控制与决策, 2004, 19(5): 485-489, 541)
    [36] Chen V C P, Tsui K L, Barton R R, Meckesheimer M. A review on design, modeling and applications of computer experiments. IIE Transactions, 2006, 38(4): 273-291
    [37] Fang K T, Li R Z, Sudjianto A. Design and Modeling for Computer Experiments. London: Chapman and Hall/CRC, 2006
    [38] Kleijnen J P C[Author], Zhang Lie-Gang, Zhang Jian-Kang, Liu Xing-Ke[Translator]. Design and Analysis of Simulation Experiments. Beijing: Electronic Industry Press, 2010 (Kleijnen J P C[著], 张列刚, 张建康, 刘兴科[译]. 仿真实验设计与分析. 北京: 电子工业出版社, 2010)
    [39] Kleijnen J P C, Sanchez S M, Lucas T W, Cioppa T M. State-of-the-art review: a user's guide to the brave new world of designing simulation experiments. INFORMS Journal on Computing, 2005, 17(3): 263-289
    [40] Steiger N M, Lada E K, Wilson J R, Joines J A, Alexopoulos C, Goldsman D. ASAP3: a batch means procedure for steady-state simulation analysis. ACM Transactions on Modeling and Computer Simulation, 2005, 15(1): 39-73
    [41] Alexopoulos C, Goldsman D. To batch or not to batch? ACM Transactions on Modeling and Computer Simulation, 2004, 14(1): 76-114
    [42] Loeppky J L, Sacks J, Welch W J. Choosing the sample size of a computer experiment: a practical guide. Technometrics, 2009, 51(4): 366-376
    [43] Dong Zhi-Yong. Experimental Economics. Beijing: Peiking University Press, 2008 (董志勇. 实验经济学. 北京: 北京大学出版社, 2008)
    [44] Wang Fei-Yue. Web social media in disaster reduction and emergence management. Science and Technology Review, 2008, 26(10): 30-31 (王飞跃. 万维社会媒体在防灾应急中的作用. 科技导报, 2008, 26(10): 30-31)
    [45] Louie M A, Carley K M. Balancing the criticisms: validating multi-agent models of social systems. Simulation Modelling Practice and Theory, 2008, 16(2): 242-256
    [46] Bernardes A T, Stauffer D, Kertsz J. Election results and the Sznajd model on Barabasi network. The European Physical Journal B, 2002, 25(1): 123-127
    [47] Hodges J S. Six (or so) things you can do with a bad model. Operations Research, 1991, 39(3): 355-365
    [48] Macal C M, North M J. Tutorial on agent-based modelling and simulation. Journal of Simulation, 2010, 4(3): 151-162
    [49] Chan W K V, Young-Jun S, Macal C M. Agent-based simulation tutorialsimulation of emergent behavior and differences between agent-based simulation and discrete-event simulation. In: Proceedings of the 2010 Winter Simulation Conference. Baltimore, Maryland: Winter Simulation Conference, 2010. 135-150
    [50] Metzger M, Polakow G. A survey on applications of agent technology in industrial process control. IEEE Transactions on Industrial Informatics, 2011, 7(4): 570-581
    [51] Jonker C M, Treur J. A formal approach to building compositional agent-based simulations. Simulating Social Complexity. Berlin, Heidelberg: Springer, 2013. 57-94
    [52] Sotomayor M, Schulten K. Single-molecule experiments in vitro and in silico. Science, 2007, 316(5828): 1144-1148
    [53] Goldenberg J, Libai B, Muller E. Talk of the network: a complex systems look at the underlying process of word-of-mouth. Marketing Letters, 2001, 12(3): 211-223
    [54] Kempe D, Kleinberg J, Tardos E. Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM International Conference on Knowledge Discovery and Data Mining. Washington, USA: ACM, 2003. 137-146
    [55] Toni F, Bentahar J. Computational logic-based agents. Autonomous Agents and Multi-Agent Systems, 2008, 16(3): 211-213
    [56] Su K L, Sattar A, Lin H, Reynolds M. A modal logic for beliefs and pro attitudes. In: Proceedings of the 22nd National Conference on Artificial Intelligence, Volume 1. Vancouver, British Columbia, Canada: AAAI, 2007. 496-501
    [57] Wooldridge M J. The Logical Modelling of Computational Multi-agent Systems[Ph.D. dissertation], University of Mancheste, Oxford, UK, 1992
    [58] Fisher M. Temporal development methods for agent-based. Autonomous Agents and Multi-Agent Systems, 2005, 10(1): 41-66
    [59] Macal C M, North M J. Tutorial on agent-based modelling and simulation. Journal of Simulation, 2010, 4(3): 151-162
    [60] Granovetter M, Soong R. Threshold models of diffusion and collective behavior. The Journal of Mathematical Sociology, 1983, 9(3): 165-179
    [61] Hasan S, Ukkusuri S V. A threshold model of social contagion process for evacuation decision making. Transportation Research, Part B: Methodological, 2011, 45(10): 1590-1605
    [62] Sznajd-Weron K, Sznajd J. Opinion evolution in closed community. International Journal of Modern Physics C, 2000, 11(6): 1157-1165
    [63] Timpanaro A M, Prado C P C. Connections between the Sznajd model with general confidence rules and graph theory. Physical Review E, 2012, 86(4): 046109
    [64] Crokidakis N. Effects of mass media on opinion spreading in the Sznajd sociophysics model. Physica A: Statistical Mechanics and Its Applications, 2012, 391(4): 1729-1734
    [65] Deffuant G, Neau D, Amblard F, Weisbuch G. Mixing beliefs among interacting agents. Advances in Complex Systems, 2000, 3(1-4): 87-98
    [66] Grauwin S, Jensen P. Opinion group formation and dynamics: structures that last from nonlasting entities. Physical Review E, 2012, 85(6): 066113
    [67] Young H P. The dynamics of social innovation. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(Supplement 4): 21285-21291
    [68] Dixit A K, Meyersson Milgrom E M, Milgrom P R. Dynamics of social, political, and economic institutions. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(Supplement 4): 21283-21284
    [69] Redner S. A Guide to First-passage Processes. Cambridge: Cambridge University Press, 2001
    [70] Durrett R, Gleeson J P, Lloyd A L, Mucha P J, Shi F, Sivakoff D, Socolar J E S, Varghese C. Graph fission in an evolving voter model. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(10): 3682-3687
    [71] Galam S. Minority opinion spreading in random geometry. The European Physical Journal BCondensed Matter and Complex Systems, 2002, 25(4): 403-406
    [72] Zheng X L, Zhong Y G, Zeng D, Wang F Y. Social influence and spread dynamics in social networks. Frontiers of Computer Science, 2012, 6(5): 611-620
    [73] Bernardes A T, Stauffer D, Kertsz J. Election results and the Sznajd model on Barabasi network. The European Physical Journal BCondensed Matter and Complex Systems, 2002, 25(1): 123-127
    [74] Suchecki K, Eguluz V M, Miguel S M. Voter model dynamics in complex networks: Role of dimensionality, disorder, and degree distribution. Physical Review E, 2005, 72(3): 036132
    [75] Castellano C, Vilone D, Vespignani A. Incomplete ordering of the voter model on small-world networks. Europhysics Letters, 2003, 63(1): 153-158
    [76] Sood V, Redner S. Voter model on heterogeneous graphs. Physical Review Letters, 2005, 94(17): 178701
    [77] De Smith M J, Goodchild M F, Longley P A. Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. Leicester: Troubador Publishing, 2009
    [78] Balcan D, Colizza V, Goncalves B, Hu H, Ramasco J J, Vespignani A. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(51): 21484-21489
    [79] Guo D, Ren B, Wang C. Integrated agent-based modeling with GIS for large scale emergency simulation. Advances in Computation and Intelligence, 2008, 5370: 618-625
    [80] Robinson C D, Brown D E. First responder information flow simulation: a tool for technology assessment. In: Proceedings of the 37th Conference on Winter Simulation. Orlando, Florida: Winter Simulation Conference, 2005. 919-925
    [81] Taillandier P, Vo D A, Amouroux E, Drogoul A. GAMA: a simulation platform that integrates geographical information data, agent-based modeling and multi-scale control. In: Proceedings of the 2012 Principles and Practice of Multi-Agent Systems. Berlin, Heidelberg: Springer, 2012. 242-258
    [82] Nssle T, Kleiner A, Brenner M. Approaching urban disaster reality: the ResQ firesimulator. In: Proceedings of the 2004 RoboCup: Robot Soccer World Cup VIII. Berlin, Heidelberg: Springer, 2005. 474-482
    [83] Tanigawa M, Takahashi T, Koto T, Takeuchi I, Noda I. Urban flood simulation as a component of integrated earthquake disaster simulation. In: Proceedings of the 2005 IEEE International Safety, Security and Rescue Robotics Workshop. Kobe, Japan: IEEE, 2005. 248-252
    [84] Carley K M, Altman N, Kaminsky B, Nave D, Yahja A. BioWar: A City-Scale Multi-Agent Network Model of Weaponized Biological Attacks, DTIC Document, CMU-ISRI-04-101, Carnegie-Mellon University, USA, 2004
    [85] Sanchez S M. Work smarter, not harder: guidelines for designing simulation experiments. In: Proceedings of the 39th Conference on Winter Simulation. Piscataway, NJ: IEEE, 2007. 84-94
    [86] Koehler J R, Owen A B. Computer experiments. Handbook of Statistics. New York: Elsevier Science, 1996. 261-308
    [87] Kleijnen J C, Beers W, Nieuwenhuyse I. Expected improvement in efficient global optimization through bootstrapped kriging. Journal of Global Optimization, 2012, 54(1): 59-73
    [88] Jin R, Chen W, Simpson T W. Comparative studies of metamodelling techniques under multiple modelling criteria. Structural and Multidisciplinary Optimization, 2001, 23(1): 1-13
    [89] Morris M D, Mitchell T J, Ylvisaker D. Bayesian design and analysis of computer experiments: use of derivatives in surface prediction. Technometrics, 1993, 35(3): 243-255
    [90] Simpson T W, Poplinski J D, Koch P N, Allen J K. Metamodels for computer-based engineering design: survey and recommendations. Engineering with Computers, 2001, 17(2): 129-150
    [91] Staelin C. Parameter Selection for Support Vector Machines, Technical Report HPL-2002-354R1, Hewlett-Packard Company, 2003
    [92] McKay M D, Beckman R J, Conover W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 2000, 42(1): 55-61
    [93] Fang K T, Lin D K J, Winker P, Zhang Y. Uniform design: theory and application. Technometrics, 2000, 42(3): 237- 248
    [94] Deutsch J L, Deutsch C V. Latin hypercube sampling with multidimensional uniformity. Journal of Statistical Planning and Inference, 2012, 142(3): 763-772
    [95] Morgan J P, Deng X W. Experimental design. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2012, 2(2): 164-172
    [96] Ning J H, Zhou Y D, Fang K T. Discrepancy for uniform design of experiments with mixtures. Journal of Statistical Planning and Inference, 2011, 141(4): 1487-1496
    [97] Lee E[Author], Chen Jia-Ding, Dai Zhong-Wei[Translator]. Statistical Methods for Survival Data Analysis. Beijing: China Statistics Press, 1998 (Lee E[著], 陈家鼎, 戴中维[译]. 生存数据分析的统计方法. 北京: 中国统计出版社, 1998)
    [98] Bettonvil B, Kleijnen J P C. Searching for important factors in simulation models with many factors: sequential bifurcation. European Journal of Operational Research, 1997, 96(1): 180-194
    [99] Kleijnen J P C, van Beers W C M. Application-driven sequential designs for simulation experiments: Kriging metamodelling. Journal of the Operational Research Society, 2004, 55(8): 876-883
    [100] Fomel S, Hennenfent G. Reproducible computational experiments using SCons. In: Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing. Austin, Texas: IEEE, 2007. 1257-1260
    [101] Gil Y, Ratnakar V, Deelman E, Mehta G, Kim J. Wings for pegasus: creating large-scale scientific applications using semantic representations of computational workflows. In: Proceedings of the 19th National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press, 2007. 1767-1774
    [102] Gil Y, Ratnakar V, Kim J, Gonzlez-Calero P A, Groth P T, Moody J, Deelman E. Wings: intelligent workflow-based design of computational experiments. IEEE Intelligent Systems, 2011, 26(1): 62-72
    [103] Maechling P, Chalupsky H, Dougherty M, Deelman E, Gil Y, Gullapalli S, Gupta V, Kesselman C, Kim J, Mehta G, Mendenhall B, Russ T, Singh G, Spraragen M, Staples G, Vahi K. Simplifying construction of complex workflows for non-expert users of the Southern California Earthquake Center Community Modeling Environment. ACM SIGMOD Record, 2005, 34(3): 24-30
    [104] Deelman E, Callaghan S, Field E, Francoeur H, Graves R, Gupta V, Jordan T H, Kesselman C, Maechling P, Mehta G, Okaya D, Vahi K, Zhao L. Managing large-scale workflow execution from resource provisioning to provenance tracking: The cybershake example. In: Proceedings of the 2nd IEEE International Conference on e-Science and Grid Computing. Washington D.C., USA: IEEE, 2006. 4-6
    [105] Page B, Knaak N, Kruse A. A discrete event simulation framework for agent-based modelling of logistic systems. In: Proceedings of the 2007 GI Jahrestagung. Bremen, German, 2007. 397-404
    [106] Wang K, Shen Z. A GPU-based parallel genetic algorithm for generating daily activity plans. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(3): 1474-1480
    [107] Wang F Y. Toward a paradigm shift in social computing: the ACP approach. IEEE Intelligent Systems, 2007, 22(5): 65-67
    [108] Lun Shu-Xian. Research on the classification of parallel execution modes of ACP theory. Acta Automatica Sinica, 2012, 38(10): 1602-1608 (伦淑娴. ACP理论的平行执行方式分类研究. 自动化学报, 2012, 38(10): 1602-1608)
    [109] Wang F Y. A big-data perspective on AI: Newton, Merton, and analytics intelligence. IEEE Intelligent Systems, 2012, 27(5): 2-4
    [110] Wang Fei-Yue. Parallel control: a method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4): 293-302 (王飞跃. 平行控制: 数据驱动的计算控制方法. 自动化学报, 2013, 39(4): 293-302)
    [111] Wang Peng, Chen Sen. A parallel control approach to optimization of information security management measures. Acta Automatica Sinica, 2011, 37(11): 1351-1355 (王鹏, 陈森. 基于平行控制的信息安全管理措施仿真与优化. 自动化学报, 2011, 37(11): 1351-1355)
    [112] Huang Wen-De, Wang Wei, Xu Xin, Xi Xiao-Ning. Computational experiments for abort planning of manned lunar landing mission based on ACP approach. Acta Automatica Sinica, 2012, 38(11): 1794-1803 (黄文德, 王威, 徐昕, 郗晓宁. 基于ACP方法的载人登月中止规划的计算实验研究. 自动化学报, 2012, 38(11): 1794-1803)
    [113] Xiong G, Dong X S, Fan D, Zhu F H, Wang K F, Lv Y S. Parallel traffic management system and its application to the 2010 Asian Games. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1): 225-235
    [114] Zhu F H, Wen D, Chen S H. Computational traffic experiments based on artificial transportation systems: an application of ACP approach. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1): 189-198
    [115] Wang Fei-Yue. Study on cyber-enabled social movement organizations based on social computing and parallel systems. Journal of University of Shanghai for Science and Technology, 2011, 33(1): 8-17 (王飞跃. 基于社会计算和平行系统的动态网民群体研究. 上海理工大学学报, 2011, 33(1): 8-17)
    [116] Zhou Yun, Qiao Hai-Quan, Qiu Xiao-Gang, Huang Ke-Di, Hu De-Wen. Research and implement on dynamic simulation engine for society computing experiments. Journal of National University of Defense Technology, 2011, 33(4): 163-167 (周云, 乔海泉, 邱晓刚, 黄柯棣, 胡德文. 社会计算实验动态仿真引擎的研究与实现. 国防科技大学学报, 2011, 33(4): 163-167)
    [117] Cheng Chang-Jian, Cui Feng, Li Le-Fei, Xiong Gang, Zou Yu-Min, Liao Chang-Yong. Parallel management systems for complex productions systems: methods and cases. Complex Systems and Complexity Science, 2010, 7(1): 24-32 (程长建, 崔峰, 李乐飞, 熊刚, 邹余敏, 廖昌勇. 复杂生产系统的平行管理方法与案例. 复杂系统与复杂性科学, 2010, 7(1): 24-32)
    [118] Xue Xiao, Wang Yang. ACP-based research on complexity of cluster supply chain. Computer Engineering and Design. 2011, 32(12): 4030-4034 (薛霄, 王杨. 基于ACP的集群式供应链复杂性研究. 计算机工程与设计, 2011, 32(12): 4030-4034)
    [119] Zheng X L, Ke G Y, Zeng D D, Ram S, Hao L. Next-generation team-science platform for scientific collaboration. IEEE Intelligent Systems, 2011, 26(6): 72-76
    [120] Zeng D, Wang F Y, Zheng X L, Yuan Y, Chen G Q, Chen J. Intelligent-commerce research in China. IEEE Intelligent Systems, 2008, 23(6): 14-18
    [121] Zheng Xiao-Long, Zhong Yong-Guang, Wang Fei-Yue, Zeng Da-Jun, Zhang Qing-Peng, Cui Kai-Nan. Social dynamics research based on web information. Complex Systems and Complexity Science, 2011, 8(3): 1-12 (郑晓龙, 钟永光, 王飞跃, 曾大军, 张清鹏, 崔凯楠. 基于网络信息的社会动力学研究. 复杂系统与复杂性科学, 2011, 8(3): 1-12)
    [122] Sanchez S M, Lucas T W, Sanchez P J, Nannini C J, Wan H. Designs for large-scale simulation experiments, with applications to defense and homeland security. Design and Analysis of Experiments: Special Designs and Applications, Volume 3. Hoboken, NJ, USA: John Wiley and Sons, Inc, 2012. 413-441
    [123] Wang Y Z, Zeng D, Cao Z D, Wang Y, Song H B, Zheng X L. The impact of community structure of social contact network on epidemic outbreak and effectiveness of non-pharmaceutical interventions. In: Proceedings of the 6th Pacific Asia Conference on Intelligence and Security Informatics. Berlin, Heidelberg: Springer, 2011. 108-120
    [124] Duan W, Cao Z D, Ge Y Z, Qiu X G. Modeling and simulation for the spread of H1N1 influenza in school using artificial societies. In: Proceedings of the 6th Pacific Asia Conference on Intelligence and Security Informatics. Berlin, Heidelberg: Springer, 2011. 121-129
    [125] Tan Z W, Mao W J, Zeng D, Li X C, Bao X G. Acquiring netizen group's opinions for modeling food safety events. In: Proceedings of the 2012 IEEE International Conference on Intelligence and Security Informatics. Arlington, VA: IEEE, 2012. 114-119
    [126] Chen W Y, Li X, Zeng D. Estimating collective belief in fixed odds betting. In: Proceedings of the 6th Pacific Asia Conference on Intelligence and Security Informatics. Berlin, Heidelberg: Springer, 2011. 54-63
    [127] Wang Fei-Yue, Jiang Zheng-Hua, Dai Ru-Wei. Population studies and artificial societies: a discussion of artificial population systems and their applications. Complex Systems and Complexity Science, 2005, 2(1): 1-9 (王飞跃, 蒋正华, 戴汝为. 人口问题与人工社会方法: 人工人口系统的设想与应用. 复杂系统与复杂性科学, 2005, 2(1): 1-9)
    [128] Miao Qing-Hai. Design of Artificial Transportation Systems Based on Srtience[Ph.D. dissertation], Institute of Automation, Chinese Academy of Sciences, China, 2007(缪青海. 基于Artience的人工交通系统研究与设计[博士学位论文], 中国科学院自动化研究所, 中国, 2007)
    [129] Zhao Hong-Xia. A Study on Computational Experiments of Relationship between Artificial Population and Travel Demand [Ph.D. dissertation], Institute of Automation, Chinese Academy of Sciences, China, 2009 (赵红霞. 人工人口与交通需求关系计算实验研究[博士学位论文], 中国科学院自动化研究所, 中国, 2009)
    [130] Lv Yi-Sheng, Ou Yan, Tang Shu-Ming, Zhu Feng-Hua, Zhao Hong-Xia. Computational experiments of evaluating road network traffic conditions based on artificial transportation systems. Journal of Jilin University (Engineering and Technology Edition), 2009, 39(S2): 87-90 (吕宜生, 欧彦, 汤淑明, 朱凤华, 赵红霞. 基于人工交通系统的路网交通运行状况评估的计算实验. 吉林大学学报 (工学版), 2009, 39(S2): 87-90)
    [131] Tang Shu-Ming. A Preliminary Study for Basic Approaches in Artificial Transportation Systems[Ph.D. dissertation], Institute of Automation, Chinese Academy of Sciences, China, 2005 (汤淑明. 人工交通系统的基本方法研究[博士学位论文], 中国科学院自动化研究所,中国, 2005)
    [132] Zhu F H, Li G X, Li Z J, Chen C, Wen D. A case study of evaluating traffic signal control systems using computational experiments. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4): 1220-1226
    [133] Zhu Feng-Hua. A Study on the Evaluation of Urban Traffic Signal Control System Based on Artificial Transportation Systems[Ph.D. dissertation], Institute of Automation, Chinese Academy of Sciences, China, 2008 (朱凤华. 基于人工交通系统的城市交通信号控制系统评价研究[博士学位论文], 中国科学院自动化研究所,中国, 2008)
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出版历程
  • 收稿日期:  2012-12-27
  • 修回日期:  2013-04-15
  • 刊出日期:  2013-08-20

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