基于免疫原理的大型复杂工程项目运营期决策理论与支持系统研究
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摘要
大型复杂工程项目无论开始阶段还是建设阶段,乃至项目运营阶段,最重要也是最频繁的活动之一是决策。决策包括信息采集、建模和分析等关键技术。决策就是从多个选择中选出具有相当合理性的一项,其过程通常是广义上的解决问题的过程,有关复杂系统(CS)的决策是各专业的决策者面临的一个共同问题。尽管各种决策的设置中包括典型的不完整、不明确的信息,专家们常常还是要据此作出好的决策,这也就是为什么需要跨学科的方法进行研究。与众多的技术、社会复杂性交织在一起,使本来就复杂的系统在决策过程中面临诸多困难,例如信息获取、决策模型制定,决策结果评价等等。
多数现实生活中的大型工程问题都可看做是复杂系统,工程本身和管理过程拥有一些特定的特征,通过分析大型复杂工程项目的技术复杂性、社会人干系复杂性,我们得知,由于各种资源的限制、管理体制的僵化以及许多复杂的条例,重大工程项目通常在运营管理上受到严重的制约,为此我们需要寻求一种新的管理模式运用到实际工程当中。“重大工程科学决策、管理创新与可持续发展”成为当下本学科主要的议题,当前需要从重大工程管理实施中面临的实际问题出发,深入开展重大工程认识论与管理方法论、重大工程管理模式与决策机制、重大工程组织创新与精益建造、重大工程国际化战略、重大工程全寿命周期与可持续发展等主题研究。
人工免疫系统(AIS)是一个自适应系统,受生物免疫学理论的启发,借鉴免疫功能、原则和模型,应用于解决实际问题的系统。在人工免疫算法中,被求解的问题视为抗原,抗体则对应于问题的解,改进的人工免疫算法与GA相似,一些人工免疫算法也是从随机生成的初始解群出发,采用复制、交叉、变异等算子进行操作,产生比父代优越的子代,这样循环执行,逐渐逼近最优解。
本研究的主要贡献如下：
(1)将生物学免疫原理运用到大型复杂项目决策过程中,分析了大型复杂项目决策信息获取存在争议的原因,隐喻免疫原理中抗体更新的过程,通过免疫系统识别自我非我的能力将决策者关心的信息从大量信息中提取,用以区分表示不同用户或不同时间段内决策者对信息的不同兴趣,起到信息过滤的作用。借鉴免疫过程中抵御有害抗原的算法,通过免疫计算估计人工免疫系统中的能量误差,排除运营维护期间同类解决方案中有较大风险的方案,改善了大型复杂工程项目面临决策复杂困难的机制,大型复杂工程项目的运营期多目标决策得到了生物免疫原理中免疫算法的成功隐喻,对各多目标决策优化问题的解决展现了免疫算法自适应的优势。
(2)将多目标决策方法与免疫原理紧密结合,创新性的运用了免疫理论相关算法解决了大型复杂工程项目运营决策中涉及的资源受限优化问题,在任务工期预先指定最后期限内使得总资源成本最小化,计算方法和理念使得资源紧张的约束条件下项目调度问题的各种精确解和启发式程序得到了发展。
(3)借鉴生物免疫的主动防御功能,运用危险理论解决了船舶撞桥的风险预测,根据获取的航行数据由危险模式的免疫算法给出危险信号,启动应急准备,改变了目前“应急”决策的被动模式,对大型复杂工程项目在突发事件状态下的应急决策问题,以实例给出了计算步骤,为运营管理者提供了主动的避险决策技术支持,转变了大型桥梁管理方被动接受损失的尴尬处境。
(4)开发了基于多理论、多模型、多目标的大型复杂工程项目运营期智能辅助决策支持系统,并首次运用到实际大型跨海桥梁的运营维护决策管理工作中,根据养护管理需求建立的专业数据库,解决了数据来源多样性难题,研究建立了桥梁养护管理多决策模型的组合,突破了单一模型决策的局限,为决策者提供了技术支持,转变了运营管理工作模式,提升了管理效率,并在其他大型桥梁运营管理中得到了较好的应用推广,经专家鉴定,该项研究成果填补了国内的行业空白,成果达到了国际先进水平。
最后,研究提出了关于免疫原理在实际应用中有待改进的建议和思路,为他人的研究提供参考。
Decision-making is one of the most important and frequent active in large and complex projects with its Start-up phase, construction phase, and the project operational phase. Decision-making's key technologys include the information gathering, modeling, and analysis. Decision-maker selected one from the multiple choice with considerable justification, the process is usually on the generalized problem-solving process, complex systems (CS) decision is a common problem of professional policy makers. Despite the various policy settings include typical incomplete and unclear information, but experts want to make good decisions, which is why the need for an interdisciplinary approach to research. With many intertwined technical, social complexity, complex systems is faced many difficulties in the decision-making process, such as access to information, decision-making model developed, result assessment, and so on.
Mega project question may be as a complex system in real life, project itself and the management process has some specific characteristics, through analysis large-scale complex engineering project technical complexity, and the social stakeholder complexity, we knew, the important engineering project usually receives the serious restriction in the operation management, because of result of resources limited, the management system ossification as well as many complex rules. We need to seek one kind of new management pattern to utilize the actual project. Science decision-making of important engineering, management innovation and sustainable development immediately become a main subject in this field. We need face the actual problem of the important project management implementation, develop the important project epistemology and the management methodology, and the important project management pattern, important project subject research and so on organization innovation and fine profit construction, important project internationalization strategy, important project total life cycle and sustainable development.
Artificial immune system (AIS) is an adaptive system, inspired by the biological theory of Immunology, immune function, principles and models for reference, should be used to solve practical problems of the system. Artificial immune algorithm, solving issues as antigens, antibodies was correspond to the solutions of problem, improved artificial immune algorithm similar to GA, some artificial immune algorithm is also starting from the randomly generated initial solution group, executing operation through copy, crossover and mutation, The new population surpasses the father generation, and is gradually approach the optimal solution.
This research main contribution is as follows:
(1) Utilized the biology immunity principle in the project complex project decision-making process, analyzed the reason of decision information existence dispute in its gather progressing, in metaphor the antibody update progress of immunity principle, extract the information which decision-makers concerned with, from the large amount of information by the immune system to recognize the ability of self-nonself, to distinguish between the different interests of different users or different periods, and decision-makers, play the role of information filtering, draw against harmful antigens in the immune process algorithm to calculate the estimated energy error of artificial immune system, excluded the solution which withing greater risk in similar solutions of operation and maintenance. Improved the situation of difficult decision-making of large-scale complex engineering project. the target decision of operation of mega projects drew on immune algorithm of biological immunity principle, problem solving of optimized for multi-objective decision making demonstrated the advantages of immune algorithm.
(2) Closely integrated multi-objective decision-making methods and immune principle, innovatively used the immune theory algorithm to solve the optimization problem of limited resources involved in mega projects operational decisions, so that the total minimum cost of resources within the pre-specified deadline task duration, the heuristic procedures of project scheduling problem under tight resource constrained have been developed with calculation methods and philosophy(to see Chapter6section2and3).
(3) First learn from active defense capabilities of biological immune system, used the Danger Theory to solve the the emergency decision problem of large and complex projects in the state of emergency, send the danger signal according to the gain navigation data by the immunity algorithm dangerous pattern, changed the passive mode of emergency decision, given the calculation procedure of avoid the ship collision with bridge, provided proactive hedging decision support for operational managers, give solution of the risk of preventing ship collision with bridges, change the awkward position of passive acceptance of loss for the manager of large bridges.
(4) Developed an intelligent decision support system based on multi-theory, multi-model, multi-objective for operation period of large and complex projects, and for the first time applied to actual large-scale cross-sea bridge's management of operation and maintenance decision-making, according to the conservation and management needs to establish professional database and solve the diversity problem of data sources to study the establishment of a decision-making model of bridge maintenance and management of multi-combination, broke through the limitations of a single model of decision-making, provided technical support for the decision-makers, changes the mode of operation and management, and enhance management efficiency, and better promote the application of the operational management of other large bridges, the experts identified the research filled the domestic blank, the results reached the international advanced level.
Finally, the researchers put forward suggestions and ideas on the improvement of immune principle, to provide a reference for others in practical applications.
多数现实生活中的大型工程问题都可看做是复杂系统,工程本身和管理过程拥有一些特定的特征,通过分析大型复杂工程项目的技术复杂性、社会人干系复杂性,我们得知,由于各种资源的限制、管理体制的僵化以及许多复杂的条例,重大工程项目通常在运营管理上受到严重的制约,为此我们需要寻求一种新的管理模式运用到实际工程当中。“重大工程科学决策、管理创新与可持续发展”成为当下本学科主要的议题,当前需要从重大工程管理实施中面临的实际问题出发,深入开展重大工程认识论与管理方法论、重大工程管理模式与决策机制、重大工程组织创新与精益建造、重大工程国际化战略、重大工程全寿命周期与可持续发展等主题研究。
人工免疫系统(AIS)是一个自适应系统,受生物免疫学理论的启发,借鉴免疫功能、原则和模型,应用于解决实际问题的系统。在人工免疫算法中,被求解的问题视为抗原,抗体则对应于问题的解,改进的人工免疫算法与GA相似,一些人工免疫算法也是从随机生成的初始解群出发,采用复制、交叉、变异等算子进行操作,产生比父代优越的子代,这样循环执行,逐渐逼近最优解。
本研究的主要贡献如下：
(1)将生物学免疫原理运用到大型复杂项目决策过程中,分析了大型复杂项目决策信息获取存在争议的原因,隐喻免疫原理中抗体更新的过程,通过免疫系统识别自我非我的能力将决策者关心的信息从大量信息中提取,用以区分表示不同用户或不同时间段内决策者对信息的不同兴趣,起到信息过滤的作用。借鉴免疫过程中抵御有害抗原的算法,通过免疫计算估计人工免疫系统中的能量误差,排除运营维护期间同类解决方案中有较大风险的方案,改善了大型复杂工程项目面临决策复杂困难的机制,大型复杂工程项目的运营期多目标决策得到了生物免疫原理中免疫算法的成功隐喻,对各多目标决策优化问题的解决展现了免疫算法自适应的优势。
(2)将多目标决策方法与免疫原理紧密结合,创新性的运用了免疫理论相关算法解决了大型复杂工程项目运营决策中涉及的资源受限优化问题,在任务工期预先指定最后期限内使得总资源成本最小化,计算方法和理念使得资源紧张的约束条件下项目调度问题的各种精确解和启发式程序得到了发展。
(3)借鉴生物免疫的主动防御功能,运用危险理论解决了船舶撞桥的风险预测,根据获取的航行数据由危险模式的免疫算法给出危险信号,启动应急准备,改变了目前“应急”决策的被动模式,对大型复杂工程项目在突发事件状态下的应急决策问题,以实例给出了计算步骤,为运营管理者提供了主动的避险决策技术支持,转变了大型桥梁管理方被动接受损失的尴尬处境。
(4)开发了基于多理论、多模型、多目标的大型复杂工程项目运营期智能辅助决策支持系统,并首次运用到实际大型跨海桥梁的运营维护决策管理工作中,根据养护管理需求建立的专业数据库,解决了数据来源多样性难题,研究建立了桥梁养护管理多决策模型的组合,突破了单一模型决策的局限,为决策者提供了技术支持,转变了运营管理工作模式,提升了管理效率,并在其他大型桥梁运营管理中得到了较好的应用推广,经专家鉴定,该项研究成果填补了国内的行业空白,成果达到了国际先进水平。
最后,研究提出了关于免疫原理在实际应用中有待改进的建议和思路,为他人的研究提供参考。
Decision-making is one of the most important and frequent active in large and complex projects with its Start-up phase, construction phase, and the project operational phase. Decision-making's key technologys include the information gathering, modeling, and analysis. Decision-maker selected one from the multiple choice with considerable justification, the process is usually on the generalized problem-solving process, complex systems (CS) decision is a common problem of professional policy makers. Despite the various policy settings include typical incomplete and unclear information, but experts want to make good decisions, which is why the need for an interdisciplinary approach to research. With many intertwined technical, social complexity, complex systems is faced many difficulties in the decision-making process, such as access to information, decision-making model developed, result assessment, and so on.
Mega project question may be as a complex system in real life, project itself and the management process has some specific characteristics, through analysis large-scale complex engineering project technical complexity, and the social stakeholder complexity, we knew, the important engineering project usually receives the serious restriction in the operation management, because of result of resources limited, the management system ossification as well as many complex rules. We need to seek one kind of new management pattern to utilize the actual project. Science decision-making of important engineering, management innovation and sustainable development immediately become a main subject in this field. We need face the actual problem of the important project management implementation, develop the important project epistemology and the management methodology, and the important project management pattern, important project subject research and so on organization innovation and fine profit construction, important project internationalization strategy, important project total life cycle and sustainable development.
Artificial immune system (AIS) is an adaptive system, inspired by the biological theory of Immunology, immune function, principles and models for reference, should be used to solve practical problems of the system. Artificial immune algorithm, solving issues as antigens, antibodies was correspond to the solutions of problem, improved artificial immune algorithm similar to GA, some artificial immune algorithm is also starting from the randomly generated initial solution group, executing operation through copy, crossover and mutation, The new population surpasses the father generation, and is gradually approach the optimal solution.
This research main contribution is as follows:
(1) Utilized the biology immunity principle in the project complex project decision-making process, analyzed the reason of decision information existence dispute in its gather progressing, in metaphor the antibody update progress of immunity principle, extract the information which decision-makers concerned with, from the large amount of information by the immune system to recognize the ability of self-nonself, to distinguish between the different interests of different users or different periods, and decision-makers, play the role of information filtering, draw against harmful antigens in the immune process algorithm to calculate the estimated energy error of artificial immune system, excluded the solution which withing greater risk in similar solutions of operation and maintenance. Improved the situation of difficult decision-making of large-scale complex engineering project. the target decision of operation of mega projects drew on immune algorithm of biological immunity principle, problem solving of optimized for multi-objective decision making demonstrated the advantages of immune algorithm.
(2) Closely integrated multi-objective decision-making methods and immune principle, innovatively used the immune theory algorithm to solve the optimization problem of limited resources involved in mega projects operational decisions, so that the total minimum cost of resources within the pre-specified deadline task duration, the heuristic procedures of project scheduling problem under tight resource constrained have been developed with calculation methods and philosophy(to see Chapter6section2and3).
(3) First learn from active defense capabilities of biological immune system, used the Danger Theory to solve the the emergency decision problem of large and complex projects in the state of emergency, send the danger signal according to the gain navigation data by the immunity algorithm dangerous pattern, changed the passive mode of emergency decision, given the calculation procedure of avoid the ship collision with bridge, provided proactive hedging decision support for operational managers, give solution of the risk of preventing ship collision with bridges, change the awkward position of passive acceptance of loss for the manager of large bridges.
(4) Developed an intelligent decision support system based on multi-theory, multi-model, multi-objective for operation period of large and complex projects, and for the first time applied to actual large-scale cross-sea bridge's management of operation and maintenance decision-making, according to the conservation and management needs to establish professional database and solve the diversity problem of data sources to study the establishment of a decision-making model of bridge maintenance and management of multi-combination, broke through the limitations of a single model of decision-making, provided technical support for the decision-makers, changes the mode of operation and management, and enhance management efficiency, and better promote the application of the operational management of other large bridges, the experts identified the research filled the domestic blank, the results reached the international advanced level.
Finally, the researchers put forward suggestions and ideas on the improvement of immune principle, to provide a reference for others in practical applications.
引文
[1]ABIDO M A.A novel multi-objective evolutionary algorithm for solving environmental/economic dispatch problem[C]. Proceedings of the 14th PSCC[C], sevilla(spain):[s.n.],2002.
[2]Alfons van Marrewijk. Managing project culture:The case of Environ Megaproject [J]. International Journal of Project Management,2007,25 (3):290-299.
[3]Alonso O., Nino F., and Velez M. A Robust Immune Based Approach to the Iterated Prisoner's Dilemma [J]. Lecture Notes in Computer Science,2004:290-301.
[4]American Association of State Highway and Transportation Officials (AASHTO).Guidelines for Bridge Management Systems[R]. AASHTO, Washington,D.C,1992.
[5]Ayara M., Timmis J., Castro L. de, and Duncan R. Negative Selection:How to Generate Detectors[C]. In 1st International Conference on Artificial Immune Systems,2002:89-98.
[6]Ayara M., Timmis J., Castro L. de, and Duncan R. Negative Selection:How to Generate Detectors[C]. In 1st International Conference on Artificial Immune Systems,2002:89-98.
[7]Benard Aritua, Nigel J Smith, Denise Bower. Construction client multi-projects—A complex adaptive systems perspective [J]. International Journal of Project Management,2009,27 (1):72-79.
[8]Bentley P. and Timmis J. A Fractal Immune Network[C]. In G. Nicosia, V. Cutello, P. J. Bentley, and J. Timmis, editors, Proceeding of the Third Conference ICARIS,2004:133-145.
[9]Bian X. and Qiu J. Adaptive Clonal Algorithm and Its Application for Optimal PMU PlacementfC]. Proceedings of 2006 International Conference on Communications, Circuits and Systems,2006, 3:2102-2106.
[10]Blismasla N, Sher W,Thorpe A. A typology for clients'multi-project environments [J].Construction Management and Economics,2004,22 (5):357-371.
[11]Buekley J.J., Hayashi Y. Fuzzy Genetie Algorithm and Applications [J]. Fuzzy Sets and Systems, 1994,61:129-136.
[12]Burgess A R,Killebrew J B.Variation in activity level on a cyclic arrow diagram [J].Journal of Industrial Engineering,1962; 13:76-83
[13]Burgess A, Killebrew J.Variation in activity level on a cyclic arrow diagram [J]. Ind Eng 1962, (2):76-83.
[14]Burnet F.M.,. The Clonal Selection Theory of Acquired Immunity [M]. Cambridge University Press, 1959.
[15]Campelo F., Guimaraes F., Igarashi H.and Ramirez J. A Clonal Selection Algorithm for Optimization in Electromagnetics [J]. IEEE Transactions on Magnetics,2005,41(5).
[16]Castro L. de and Timmis J. An Artificial Immune Network for Multimodal Function Optimization[C]. Proceedings of IEEE Congress on Evolutionary Computation (CEC'02),2002(1):699-674.
[17]Castro L. de and Timmis J. Hierarchy and Convergence of Immune Networks:Basic Ideas and Preliminary Results[C]. In Proc. of the 1st Inter. Conf. on Artificial Immune Systems (ICARIS), 2002, September 9th-11th.
[18]Castro L. de and Timmis J.,2003. Artificial Immune Systems as a Novel Soft Computing Paradigm [J]. Soft Computing Journal,2003,7(7).
[19]Castro L. de and Zuben F. An Evolutionary Immune Network for data Clustering[C]. In Proc. of the IEEE SBRN (Brazilian Symposium on Artificial Neural Networks),2000:84-89.
[20]Castro L. de and Zuben F. Artificial Immune Systems:Part I-Basic Theory and Applications[M]. TR-DCA,1999,01.
[21]Castro L. de and Zuben F. Learning and Optimization Using the Clonal Selection Principle[J]. IEEE Transactions on Evolutionary Computation,2002,6(3):239-251.
[22]Castro L. de and Zuben F. von. AiNET:An artificial immune network for data analysis [M]. In Data Mining:A Heuristic Approach, H. A. Abbass, R. A. Saker, and C. S. Newton (eds.), Idea Group Publishing,2001:231-259.
[23]CHEN Jun, MAHFORF M. A population adaptive based immune al-gorithm for solving multi-objective optimization problems[C]. Proc of the5th International Conference on Artifical Immune Systems.2006.
[24]Chen Z. Data Mining Based on Clonal Selection Wavelet Network[C]. Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing,2007:665-669.
[25]CHU N J S, LIM J P, JUNG H K. Optimal design of synchronous motor with parameter correction using immune algorithm[J]. IEEE T ransactions on Energ y Co nversion,1999,14(3):610-615.
[26]COELLO C C A,VELD-HUIZEN D A van,LAMONT G B,et al. Evolutionary algorithms for solving multi-objective problems[M].Berlin:Springer,2002,
[27]Cutello V., Narzisi G., Nicosia G., and Pavone M. An Immunological Algorithm for Global Numerical Optimization[C]. Artificial Evolution:7th Int. Conference, Evolution Artificielle, EA 2005, October 26-28, Lille, France, Springer,2005,3871:284-295.
[28]Cutello V., Nicosia G. and Pavone M. Real Coded Clonal Selection Algorithm for Unconstrained Global Optimization using a Hybrid Inversely Proportional Hyper mutation Operator[C]. The 21st Annual ACM Symposium on Applied Computing,2006,2:950-954.
[29]D.S.Turner, J.A.Richardson. Bridge Management System Data Needs and Data Collection Characteristics of Bridge Management Systems [C]. Proc.,7th Conf.on Bridge Mgmt.,Transp. National Academy Press,1994:5-15.
[30]Dabrowski J. and Kubale M.,2008. Computer Experiments with a Parallel Clonal Selection Algorithm for the Graph Coloring Problem. IEEE International Symposium on Parallel and Distributed Processing (IPDPS 2008),14-18 April, Miami, FL, USA, pp.1-6.
[31]Danzhen G., Qian A., and Chen C. The Application of Artificial Immune Network in Load Classification[C]. Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies,6-9 April, Nanjing, China,2008:1394-1398.
[32]Dasgupta, S., Vankayala, V.K.. Developing Real Time Business Intelligence Systems[C]. In:1st Annual IEEE Systems Conference,2007.
[33]DAS S, NATARAJAN B, STEVENS D, et al,. Multi-objective and constrained optimization for DS-CDMA code design based on the clonal selection principle [J].Applied Soft Computing,2008, 8 (1):788-797.
[34]DE CAST RO L N, TIMMI S J. Artificial immune system:a new computational intelligence approach [M]. Berlin:Spinger-Ver lag,2002.
[35]Deb K, Rao U V, Karthik S. Dynamic Multi-objective Optimizationand Decision Making Using Modified NSGA-Ⅱ:A Case Study onHydro-thermal Power SchedulingfC]. Proc. of Conference onEvolutionary Multi-criterion Optimization. Berlin, Germany:Springer,2007:803-817.
[36]DEB K,AGRAWALS,PRATAP A,et al. Afast elitist non-domina-ted sorting genetic algorithm for multi-objective optimization[C].Proc of the6th International Conference on Parallel Problem Solving from Nature. London:Springer-Verlag,2000:849-858.
[37]Dioubate Mamady I基于进化免疫系统理论的多机器人协作和机器人目标探索研究[D].中南大学,2009.
[38]Dirk Pieter van Donk etc. From organising as projects to projects as organizations[J]. International Journal of Project Management.2008,26.
[39]Eom SB. Decision support systems [C].10th International Encyc10paedia of Business and Management,2001.
[40]Farmer JD, Packard NH, Perelson AS. The immune system, adaptation and machine learning [J].Physica D:Nonlinear Phenomena,1986,22 (1-3):187-204.
[41]Federal Highway Administration (FHWA).Asset Management Preserving a$1 Trillion Investment. FOCUS[R]. FHWA,2000:1-2.
[42]Flyvbjerg, B., M.K. Skamris Holm and S.L. Buhl. How (in) accurate are demand forecasts in public works projects? The case of transportation [J]. Journal of the American Planning Association,2005, 71 (2):131-177.
[43]FORREST S, HOFM EYR S A, SOM AYAJI A. Computer immunology [J]. Communications of the ACM,1997,40(10):88-96.
[44]Foster I, Kesselman C. The grid:blueprint for a new computing infrastructure [M].San Francisco: Morgan Kaufmann Publishers,1998.
[45]G.Thomas, W.Fernandez. Success in IT projects:a matter of definition [J]. International Journal of Project Management.2008,26:733-742.
[46]G.W.Hoffmann. An Neural Network Model Based on the Analogy with the Immune System [J]. Eur. J. Immunol,1986 (5):638-647
[47]GASPAR, COLLARD P. Two models of immunization for time inde-pendent optimization[C].Proc of IEEE International Conference on Systems, Man, and Cybernetics.2000:113-118.
[48]Geraldi J.G., Lee-Kelley.L., Kutsch E. The Titanic Sunk, So What-Project Manager Response to Unexpected Events [J]. International Journal of Project Management,2009, (10):8.
[49]Gigerenzer. The Psychology of Good Judgment:Frequency Formats and Simple Algorithms [J]. Medical Decision Making,1996,16(3):273-280.
[50]GOLDBERG G E, SEGRESTP. Finite Markov chain analysis of ge-netic algorithms[C]. Proc of the 2nd International Conference onn Genetic Algorithms on Genetic Algorithms and Their Application. Hillsdale:L Erlbaum Associates Inc,1987:1-8.
[51]H.Hawk, E.P.Small. The BRIDGIT Bridge Management System [J]. Structural Engineering International.1998,8(4):309-314.
[52]Hitoshi Furuta, Jianhong He, Eiichi Watanabe. A Fuzzy Expert System for Damage Assessment Using Genetic Algorithms and Neural Networks [J].Journal of Computing in Civil Engineering. 1996,11.
[53]Hoffmann G W. A neural network model based on the analogy with the immune system [J].Journal of Theoretical Biology,1986,122 (1):33-67.
[54]Hunt J et al,. Learning Using an Artificial Immune System [J]. Journal of Network and CornPuter Application,1996,19:189-212.
[55]Hwang, C. L, and Abu Syed Md Masud. Multiple Objective Decision Making, Methods And Applications:a State-of-the-art Survey [M]. Berlin:Springer-Verlag,1979.
[56]Ichizou Mikami, Shigenori Tanaka and Akira Kurachi.Expert System with Learning Ability for Retrofitting Steel Bridges [J]. Journal of Computing in Civil Engineering. ASCE,1994,8(1).
[57]Igawa K. and Ohashi H. A Negative Selection Algorithm for Classification and Reduction of the Noise Effect [J]. Appl. Soft Comput. J.2008.
[58]Ignizio, James P. Comments on the special volume:Mathematical programming with multiple objectives [J]. Computers and Operations Research,1981,8(4):355-356.
[59]J. Sullivan,R. Beach. Improving project outcomes through operational reliability:a conceptual model[J]. International Journal of Project Management.2009, vol.27:765-775.
[60]J.D. Farmer, N.H. Packard, and A. Perelson. The Immune System, Adaption, and Machine Learning [J].Physica D,1986 (22):187-204.
[61]J.de Brito, F.A.Branco, P.Thoft-Christensen, J.D.Srensen. An Expert System for Concrete Bridge Management [J]. Engineering Structure.1997,19(7).
[62]J.V.Chelsom, A.C.Payne and L.R.P. Reavill, Management for Engineers, Scientists and Technologists [M]. John Wiley & Sons,2005.
[63]James M Lyneis, David N. Ford. System dynamics applied to project management:a survey, assessment, and directions for future research [J].System Dynamics Review,2007, Vol.23 (2-3):157-189.
[64]Jarosz, P., Burczyski, T.:Coupling of immune algorithms and game theory in multiobjective optimization [J]. Artifical Intelligence and Soft Computing,2010,500-507.
[65]Jerne N K. Toward a network theory of the immune system [J].Annual of Immunology,1974,125 (2):373-389.
[66]JIAO Licheng, DU Haifeng. Development and pro spectof the artificial immune system [J]. Acta Electr onica Sinica,2003,31(10):1540-1548.
[67]JIAO Li-cheng,GONG Mao-guo,SHANG Rong-hua,et al. Clonal selection with immune dominance and energy based multiobjective op-timization[C].Proc of the3rd International Conference on Evolu-tionary Multi-Criterion Optimization. Berlin:Springer,2005:474-489.
[68]John Bennett. International Construction Project Management general theory and practice [M]. Butterworth-Heinemann Ltd,1991.
[69]Jon Timmis, Mark Neal, John Hunt. An artificial immune system for data analysis [J].Biosystems, 2000,55(1-3):143-150.
[70]Joshua D Knowles, David W Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy [J].Evolutionary Computation,2000,8 (2):149-172.
[71]Kalmanje KrishnaKumar, James Neidhoefer. Immunised Neurocontrol [J].Expert Systems With Applications,1997,13 (3):201-214.
[72]KIMJ, BENTLEY P J. A model of gene library evolution in the dy-namic clonal selection algorithm [C].Proc of the 1st International Conference on Artificial Immune Systems.2002:9-11.
[73]Knight T. and Timmis J. A Multi-Layered Immune Inspired Approach to Data Mining[C]. Proceedings of the 4th International Conference on Recent Advances in Soft Computing, 2002:266-271.
[74]Lars Taxen, Joakim Lilliesk Id. Images as action instruments in complex projects[J].International Journal of Project Management,2008,26 (5):527-536.
[75]Lee,Sang-Ho; Jeong,Yeon-Suk.A System Integration Framework through Development of ISO 10303-Based Product Model for Steel Bridges [J]. Automation in Construction.2006,15(2): 212-228.
[76]Leijten, M. Big Dig:Een halve eeuw Central Artery in Boston [J]. H. de Bruijn, G.R. Teisman, J. Edelenbos and W,2004.
[77]Levin, M.S.:Composite Systems Decisions (Decision Engineering) [M]. Springer, NewYork, Inc. 2006
[78]Lin, C. H., Chen, C. S., Wu, C. J.,& Kang, M. S. Application of immune algorithm to optimal switching operation for distribution-loss minimisation and loading balance[C]. IEE Proceedings: Generation, Transmission and Distribution,2003,150(2):183-189.
[79]Linkov I, Figueira JR, Levchenko A, Tervonen T, Tkachuk A, Satterstrom FK, Seager TP. A multi-criteria decision analysis approach for prioritization of performance metrics U.S. government performance and response act and oil spill response [J]. Managing Critical Infrastructure Risks,2007:261-298.
[80]LIU Y, XIAO R B. Optimal synthesis of mechanisms for path generatio nusing refined numerical representation based model and AIS based sear ching method [J].ASME Jo ur nal o f Mechanical Design,2005,127 (4):688-691.
[81]LU Bin, JIAO Li-cheng, DU Hai-feng, et al. IFMOA:immune for-getting multiobjective optimization algorithm[C]. Proc of the 1st In-ternational Conference on Advances in Natural Computation.2005:399-408.
[82]M. Hallgren, T. Wilson. The nature and management of crises in construction projects: projects-as-practice observations [J].International Journal of Project Management.2008, vol. 26:830-838.
[83]M.K.S derqvist, M.Veijola. The Finnish Bridge Management System [J].Structural Engineering International.1998,8(4):315-319.
[84]M.Liu, D.M.Frangopol.Optimizing Bridge Network Maintenance Management under Uncertainty with Conflicting Criteria:Life-Cycle Maintenance, Failure, and User Costs [J]. Journal of Structural Engineering.2006,132(11):1835-1845.
[85]M.R.Gholamian, S.M.T.FatemiGhomi, M.Ghazanfari.A hyrid intelligent system for multi-objective decision making Problems[J].ComPuters&Industrial Engineering,2006(51):26-43
[86]MacCormick. Judgment Analysis of Surgeons 'Prioritization of Patients for Elective General Surgery [J]. Medical Decision Making,2006,26(3):255-264.
[87]N. Lesca, ML. Caron-Fasan. Strategic scanning project failure and abandonment factors:lessons learned [J]. European Journal of Information System.2008,17:371-86.
[88]Nanas, N., Uren, V., de Roeck, A., Domingue, J.:Multi-topic information filtering with a single user profile[C]. In:3rd Hellenic Conference on Artificial Intelligence,2004:400-409.
[89]Neal M. An Artificial Immune System for Continuous Analysis of Time-Varying Data[C]. In Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS),2002, 1:76-85.
[90]P.D.Thompson, et al. The PONTIS Bridge Management System [J].Structural Engineering International.1998,8(4):303-308.
[91]ParkD, KandelA, Langholz G Genetie-Based New Fuzzy Reasoning Models with Application to Fuzzy Control[J].IEEE Transon SMC,1994,24(1):39-7.
[92]Peter E D Love, Zahir Irani, Craig Standing, Chad Lin, Janice M Burn. The Enigma of Evaluation: Benefits,Costs and Risks of FT in Australian Small Me-dium-sized Enterprises[J].Information& Manage- ment,2005, (42).
[93]R.Deaton, M.Garzon, J.A.Rose, R.C.Murphy, S.E.Stevens, D.R.Franceschetti. DNABased Artificial immune System for Self-Nonself Discrimination [J]. Proceedings of the 1997 IEEE International Conference on System, Orlando, Florida.1997.8
[94]Rechtin, E.:Systems architecting of organizations:Why eagles can't swim [M]. CRC Press,1999.
[95]S. Forrest, A. S. Perelson. Genetic algorithms and the immune system [M]. In H. Schwefeland R. Maenner, editors, Parallel Problem Solving from Nature, Berlin,1991.
[96]S. Forrest, S. Hofineyr. Computer Immunology [J]. Communications of the ACM,1997, 40(10):88-96.
[97]S. Hofmeyr, S. Forrest. Arichitecture for an Artificial Immune System [J]. Evolutionary Computation Journal,1999,7(1):45-68.
[98]S.Forrest, A.S.Perelson, L.Allen, R.Cherukuri. Self-Nonself Discrimination in a Computer [J]. Proceedings of IEEE Symposium on Research in Security and Privacy, Oakland.1994.5
[99]Sara A. McComb, Stephen G. Green, W. Dale Compton. Team flexibility's relationship to staffing and performance in complex projects:An empirical analysis [J].Journal of Engineering and Technology Management,2007, Volume 24 (Issue 4):Pages293-313.
[100]Shenhar A J,Dvir D. Project Management Research-the Chal-lenge and Opportunity[J].Project Management Journal,2007,38 (2):93-99.
[101]Simon Austin, Andrew Newton, John Steele, Paul Wasket. Modeling and managing project complexity [J].International Journal of Project Management,2002, (20):191-198.
[102]Smith G F. Managerial problem identification [J]. Omega,1989,17(1):27-36.
[103]Soderholm A. Project Management of Unexpected Events [J].International Journal of Project Management,2008,26 (1):80-86.
[104]Somayaji, Anil. Immunology, diversity, and homeostasis:The past and future of biologically inspired computer defenses [J]. Information Security Technical Report,2007,12(4):228-234.
[105]Srinivas N, Kalyanmoy D. Multiobjective optimization using nondominated sorting in genetic algorithms [J].Evolutionary Computation,1994,2 (3):221-248.
[106]Stanley Zionts, Jyrki Wallenius. An Interactive Programming Method For Solving The Multiple Criteria Problem [J]. Management Science,1976,22(6):652.
[107]Sun-Kon Kim,Kyung-Sik CHO,Jong-Hwa Kim.Development of Computerized Management System for Cable-Supported Bridge[C].IABSE Conference,Seoul,2001.
[108]TAN K C,GOHC K,MAMUN A A,et al. An evolutionary artificial immune system for multi-objective optimization [J].Artificial Intelli-gence Review,2002,187 (2):371-392.
[109]TMMIS J, KNIGHT T. Data mining:a heuristic approach [M]. Hershey:Idea Publishing Group, 2001.
[110]Timmis. J. J. Hunt, D. Cooke, M. Neal, C. King. Jisys:the development of an artificialimmune System for real world applications [M]. Springer-Verlag.1999.
[111]Turoff M, Chumer M, Van de Walle B, Yao X. The design of a dynamic emergency response management information system [J]. J Inf Tech Theor,2004, Appl 5(4):1-36
[112]Van Den Eede G, Muhren W, Smals R, Van de Walle B.Incident response information systems capability:the role of the DERMIS design premises[C]. In:Van de Walle B, Turoff M (eds) Proceedings of the 3rd international conference on information systems for crisis response and management ISCRAM 2006,251-261
[113]Van Noortwijk, J.M. A survey of the application of gamma processes in maintenance [J]. Reliability Engineering and System Safety,2009,94(1):2-21.
[114]Varela F.J., Stewart J. Dynamics of a Class of Immune Network, Global Stability ofldiotype Interactions [J]. J. theoretical Biology,1990,144.
[115]Varela, F.J., Coutinho, A. Second generation immune network [J]. Immunology Today 12,1991: 159-166.
[116]Volkema.R.J. Problem formulation in planning and design [J]. Management Seienee,1983, 29(6):639-652.
[117]WANG Xiao-lan,MAHFOUF M. ACSAMO:an adaptive multiobjec-tive optimization algorithm using the clonal selection principle[C].Proc of the2nd European Symposium on Nature inspired Smart Infor-mation Systems.2006.
[118]WATKINS A, BOGGESS L. A new classifier based on resour celimited artificial immune systems [C]. Proceedings of Congress on Evolutionary Computation[C]. Washington,2002.
[119]Whitty, S.J., Maylor, H. And then came Complex Project Management (revised) [J].International Journal of Project Management,2009, (4).
[120]XIAN Bin, CAO Hong-qiao, XU Yan-wu. Negative selection based immune optimization [J].Advances in Engineering Software Elsevier Science,2007,38 (10):649-658.
[121]XIAO R B, TAO Z W, LIU Y. Isomor phism ident ification of kinematic chains using novel evolutionary approaches [J]. ASME Journal of Computing and Information Science,2005,5(1): 18-24.
[122]Yang Lunbiao. Make an approach to the optimal solution of fuzzy non-linear programming[C].AMSE conference signal and systems.Dalian,1989,1 (9):73-81.
[123]YAO Changfeng.Research on Multi-objective Fuzzy Decision-making Method Based on GA [J]. Journal of System Simulation,2005,17(10):2341-2348.
[124]Z.Tang, Yamaguchi T, Tashima K et al. Multiple-valued Immune Network Model and Its Simualtions [J]. In:Proc 27th Int Symposium on Multiple-valued Logic. Autigonish, Canada, 1997:233-238.
[125]Zeng J., Li T., Liu X., Liu C., Peng L. and Sun F. A Feedback Negative Selection Algorithm to Anomaly Detection[C]. Third International Conference on Natural Computation,2007,3:604-608.
[126]Zitzler E, Thiele L. An evolutionary algorithm for multiobjective optimization:The strength Pareto approach[R].Technical Report 43, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH). Zurich, Switzerland:1998.
[127]白广忱.系统可意性多目标优化的满意度[J].系统工程与电了技术,2000,22(8)：85-87.
[128]曾先彬,刘克胜,王煦法.基于免疫进化规划的多层前馈网络设计[J].软件学报,1999, 10(11)：1180-1184.
[129]曹先彬,郑振,刘克胜,等.免疫进化策略及其在二次布局求解中的应用[J].计算机工程,2000,26(3)：1-10.
[130]岑健.基于人工免疫系统的机组复合故障诊断技术研究[D].华南理工大学,2010.
[131]陈婕.基于免疫算法的演化多目标优化方法研究[D].武汉理工大学,2010.
[132]陈伟锋.大规模复杂过程系统的高性能优化理论与方法研究[D].浙江大学,2011.
[133]陈文清.基于免疫机理的水泥生产工艺故障智能诊断方法研究[D].华中科技大学,2011.
[134]丁永生,任立红.人工免疫系统：理论和应用[J].模式识别与人工智能,2000,13(1)：52-59.
[135]窦振中.模糊逻辑控制技术及其应用[M].北京：北京航空航天大学出版社,1994
[136]段鹰.复杂生产系统决策若干关键技术研究[D].重庆大学,2009.
[137]樊治平,尤天慧等.属性权重信息不完全的区间数多属性决策方法[JJ.东北大学学报,2005,8：798—800.
[138]冯英浚.关于多目标最优化问题的Fuzzy解[J].科学通报,1981,17：1028-1030.
[139]高萍.基于可靠性分析的复杂设备预防性维修决策研究[D].清华大学,2008.
[140]龚固丰.人工免疫系统在非线性系统辨识与预测控制中的应用研究[D].湖南大学,2009.
[141]勾轶.基于免疫算法和多传感器信息融合的电机故障综合诊断方法研究[D].沈阳工业大学,2010.
[142]顾连胜.大型工程投资决策中风险管理的理论与应用研究[D].西安建筑科技大学,2004
[143]郭振华.大型项目建筑工程一切险承保风险评估方法[J].同济大学学报(自然科学版),2005(7),985-989.
[144]韩旭明.若干改进的人工免疫算法及其在大气环境领域中的应用[D].吉林大学,2010.
[145]何磊,戴冠中.一种基于模糊遗传算法的隶属函数自学习方法研究[J].西北工业大学学报,1997,2：88-92.
[146]胡秦生.模糊多目标规划最小隶属度偏差法[J].运筹学杂志,1995,11：38-42.
[147]胡宇博.基于多目标免疫进化算法的动态车辆路径优化研究[D].郑州大学,2009.
[148]胡志华.基于免疫系统的协同进化机制及其应用研究[D].东华大学,2009.
[149]黄席樾,张著洪,何传江,等.现代智能算法理论及应用[M].北京：科学出版社,2005.
[150]蒋静坪.计算机实时控制[M],浙江大学1川饭社,1992年4月第一版.
[151]焦李成,杜海峰.人工免疫系统进展与展望[J].电子学报,2003,31(10)：1540-1548.
[152]雷佳等,基于遗传算法的模型参考自适应控制[J].控制理论与应用,1998,15(3)：446,-468.
[153]李广强,滕弘飞,霍军周.并行混合免疫算法及其在布局设计中的应用[J].机械工程学报,2003,39(6)：79-85.
[154]李擎,郑德玲等-种新的模糊遗传算法[J].北京科技人学学报,2001,2：86-89
[155]李群育.免疫优化算法及其在投资组合中的应用研究[D].重庆大学,2004.
[156]李荣钧.模糊多准则决策理论与应用[M].北京：科学出版社,2002.
[157]李润心.基于免疫克隆选择的维数缩减及其应用[D].西安电子科技大学,2010.
[158]李涛.基于免疫的网络监控模型[J].计算机学报,2006,29(9)：1515-1522.
[159]李向华.基于人工免疫系统的增量聚类算法及其优化与应用的研究[D].吉林大学,2009.
[160]李学全,李辉.带权值的模糊多目标线性规划[J].经济数学,2003,4：81-85
[161]李中.基于人工免疫系统的电力变压器故障诊断技术研究[D].华北电力大学(河北),2010.
[162]梁意文,潘海军,康立山.免疫识别器构造的多级演化[J].小型微型计算机系统,2002,23(4)：441-443.
[163]林峰等,联姻遗传算法在CSTR系统中的应用[J].控制与决策,2001,16(4)：457～460.
[164]刘芳.基于免疫多目标优化的否定选择算法[D].西安电子科技大学,2011.
[165]刘丽珏.免疫进化算法及其在多机器人协作中的应用研究[D].中南大学,2008.
[166]刘妹琴等.实时遗传算法在位置伺服系统中的应用[J].中国电机土程学报,2000,20(2)：5—9.
[167]刘若辰,杜海峰,焦李成.免疫多克隆策略[J].计算机研究与发展,2004,41(4)：571-576.
[168]刘星宝.人工免疫系统的若干关键问题研究[D].中南大学,2010.
[169]刘玉.基于改进免疫算法的多目标优化研究[D].哈尔滨工程大学,2011.
[170]鲁慧君.重大项目的决策机制探讨[J].中国工程咨询,2004(9),7-8.
[171]栾林林.基于人工免疫算法的故障诊断方法研究[D].华东理工大学,2012.
[172]马慧敏.基于免疫理论的非常规突发事件应急管理主动免疫系统研究[D].武汉理工大学,2010.
[173]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].东北大学,2008.
[174]马丽仪,邱菀华,杨亚琴.大型复杂项目风险建模与熵决策[J].北京航空航天大学学报,2010,02：61-64.
[175]门文周,侯渡舟等.工程项目投资决策中的风险评价研究[J].西安建筑科技大学学报(白然科学版),2004(9),337-344.
[176]莫宏伟,金鸿章.人工免疫系统一个新兴的交叉学科[J].计算机工程与科学,2004,26(5)：70-73.
[177]牛慧峰.免疫机理与支持向量机复合的故障诊断理论及试验研究[D].燕山大学,2009.
[178]裴星鑫.用于组合优化的人工免疫算法设计与分析[D].中国科学技术大学,2010.
[179]彭维,黄辉先,徐建伟,李密青.一种基于记忆克隆选择的多目标免疫算法[J].计算机工程与应用,2008,16：60-63.
[180]钱淑渠,张著洪.动态多目标免疫优化算法及性能测试研究[J].智能系统学报,2007,05：72-81.
[181]饶从军,肖新平.多目标决策问题的模糊数学解法[J].武汉理工大学学报,2006,8：700—703.
[182]孙凯.基于免疫算法与分散搜索的钢铁生产调度研究[D].上海交通大学,2009.
[183]孙增析.智能控制理论与技术[M].北京：清华人学出版,1997,4.
[184]汤炎非,杨青.可行性研究与投资决策口伽.武汉：武汉大学出版社,1998.5.
[185]田玉玲.多层免疫模型及其在故障诊断中的应用研究[D].太原理工大学,2009.
[186]王洪礼,郭树起,谭欣.生化系统中时间延迟与非线性振荡研究[J].化学反应工程与工艺,1999,(04).
[187]王坚强.信息不完全的多准则决策的SIR方法[J].系统工程与电子技术,2004.26(9)：1205—1208.
[188]王炼红.人工免疫优化与分类算法及其应用研究[D].湖南大学,2009.
[189]王敏蔚.基于改进免疫算法的机组组合优化[D].浙江大学,2011.
[190]王润良,郑晓齐,孙建平.技术复杂性及其对组织结构的影响[J].科学学研究,2001,(03)：29-33.
[191]王卫民.基于人工免疫系统的否定选择算法改进相关研究[D].电子科技大学,2008.
[192]王小林.项目投资群体决策模型研究[M].北京：中国农业出版社,2005.6
[193]王晓睿.隧道软弱围岩大变形监控及免疫智能反分析[D].华中科技大学,2009.
[194]王亚奇.多传播因素的复杂网络病毒传播及免疫策略研究[D].南京邮电大学,2011.
[195]伍进伟.工程建设项目投资管理与决策分析[J].工程建设与设计,2003(4),29-31.
[196]席酉民.大型工程决策[M].贵州：贵州人民出版社,1988.3.
[197]肖人彬,王磊.人工免疫系统：原理、模型、分析及展望[J].计算机学报,2002,25(12)：1281-1293.
[198]肖人彬,周济.决策问题结构化的一个测度[J].决策与决策支持系统,1994,4(1)：64-68.
[199]肖人彬.基于免疫计算的机构轨迹综合[J].计算机辅助设计与图形学学报,2004,16(6)：812-818.
[200]萧毅鸿.基于本体的复杂决策任务表示方法与求解技术研究[D].南京大学,2011.
[201]徐立芳.免疫克隆选择算法应用研究[D].哈尔滨工程大学,2008.
[202]徐锐.人工免疫算法优化及其应用研究[D].上海大学,2009.
[203]徐泽水一种交互式多目标决策方法[J].系统工程理论与实践,2002(2)：104-108.
[204]许力CSTR系统的基于CMAC(?)申经网络的学习控制研究[J].控制与决策1992,7(2)：131-136.
[205]续欣莹.基于免疫应答原理的人工免疫算法及其应用[D].太原理工大学,2009.
[206]杨纶标.多目标非线性规划的Fuzzy解的研究[J].华南理工大学学报,1993,2.
[207]叶洪涛.基于免疫算法的污水处理系统预测及优化控制研究[D].华南理工大学,2011.
[208]叶剑锋.基于免疫优化算法的阵列天线综合的研究[D].哈尔滨工程大学,2009.
[209]尤勇,盛万兴,王孙安.基于人工免疫网络的短期负荷预测模型[J].中国电机工程学报,2003(3)：26-29.
[210]袁桂丽.人工免疫系统及其在电站控制中的应用研究[D].华北电力大学(北京),2010.
[211]原霞飞.基于人工免疫算法的电子商务推荐系统的研究[D].山东师范大学,2011.
[212]张仕彬,黄文杰.大型工程项目的投保决策方法[J].建筑管理现代化,2004(1),6-9.
[213]张欣莉.基于目标满意度的交互式多目标决策改进方法[J].系统工程,2004,22(9)：10-12.
[214]郑中义.船舶自动避碰决策系统的研究[D].大连海事大学,2002.
[215]钟成勋,刘树云.项目投资决策通论[M].呼和浩特：内蒙古大学出版社,1992.10
[216]朱道海,瞿良华.大型工程项目决策分析与环境因素[J].上海大学学报(自然科学版),2001(8),342-344.
[217]朱虎明.基于集群计算的免疫优化算法及其应用研究[D].西安电子科技大学,2010.
[218]朱玉.面向瓦斯突出预测的人工免疫算法与模型研究[D].中国矿业大学,2009.
[2]Alfons van Marrewijk. Managing project culture:The case of Environ Megaproject [J]. International Journal of Project Management,2007,25 (3):290-299.
[3]Alonso O., Nino F., and Velez M. A Robust Immune Based Approach to the Iterated Prisoner's Dilemma [J]. Lecture Notes in Computer Science,2004:290-301.
[4]American Association of State Highway and Transportation Officials (AASHTO).Guidelines for Bridge Management Systems[R]. AASHTO, Washington,D.C,1992.
[5]Ayara M., Timmis J., Castro L. de, and Duncan R. Negative Selection:How to Generate Detectors[C]. In 1st International Conference on Artificial Immune Systems,2002:89-98.
[6]Ayara M., Timmis J., Castro L. de, and Duncan R. Negative Selection:How to Generate Detectors[C]. In 1st International Conference on Artificial Immune Systems,2002:89-98.
[7]Benard Aritua, Nigel J Smith, Denise Bower. Construction client multi-projects—A complex adaptive systems perspective [J]. International Journal of Project Management,2009,27 (1):72-79.
[8]Bentley P. and Timmis J. A Fractal Immune Network[C]. In G. Nicosia, V. Cutello, P. J. Bentley, and J. Timmis, editors, Proceeding of the Third Conference ICARIS,2004:133-145.
[9]Bian X. and Qiu J. Adaptive Clonal Algorithm and Its Application for Optimal PMU PlacementfC]. Proceedings of 2006 International Conference on Communications, Circuits and Systems,2006, 3:2102-2106.
[10]Blismasla N, Sher W,Thorpe A. A typology for clients'multi-project environments [J].Construction Management and Economics,2004,22 (5):357-371.
[11]Buekley J.J., Hayashi Y. Fuzzy Genetie Algorithm and Applications [J]. Fuzzy Sets and Systems, 1994,61:129-136.
[12]Burgess A R,Killebrew J B.Variation in activity level on a cyclic arrow diagram [J].Journal of Industrial Engineering,1962; 13:76-83
[13]Burgess A, Killebrew J.Variation in activity level on a cyclic arrow diagram [J]. Ind Eng 1962, (2):76-83.
[14]Burnet F.M.,. The Clonal Selection Theory of Acquired Immunity [M]. Cambridge University Press, 1959.
[15]Campelo F., Guimaraes F., Igarashi H.and Ramirez J. A Clonal Selection Algorithm for Optimization in Electromagnetics [J]. IEEE Transactions on Magnetics,2005,41(5).
[16]Castro L. de and Timmis J. An Artificial Immune Network for Multimodal Function Optimization[C]. Proceedings of IEEE Congress on Evolutionary Computation (CEC'02),2002(1):699-674.
[17]Castro L. de and Timmis J. Hierarchy and Convergence of Immune Networks:Basic Ideas and Preliminary Results[C]. In Proc. of the 1st Inter. Conf. on Artificial Immune Systems (ICARIS), 2002, September 9th-11th.
[18]Castro L. de and Timmis J.,2003. Artificial Immune Systems as a Novel Soft Computing Paradigm [J]. Soft Computing Journal,2003,7(7).
[19]Castro L. de and Zuben F. An Evolutionary Immune Network for data Clustering[C]. In Proc. of the IEEE SBRN (Brazilian Symposium on Artificial Neural Networks),2000:84-89.
[20]Castro L. de and Zuben F. Artificial Immune Systems:Part I-Basic Theory and Applications[M]. TR-DCA,1999,01.
[21]Castro L. de and Zuben F. Learning and Optimization Using the Clonal Selection Principle[J]. IEEE Transactions on Evolutionary Computation,2002,6(3):239-251.
[22]Castro L. de and Zuben F. von. AiNET:An artificial immune network for data analysis [M]. In Data Mining:A Heuristic Approach, H. A. Abbass, R. A. Saker, and C. S. Newton (eds.), Idea Group Publishing,2001:231-259.
[23]CHEN Jun, MAHFORF M. A population adaptive based immune al-gorithm for solving multi-objective optimization problems[C]. Proc of the5th International Conference on Artifical Immune Systems.2006.
[24]Chen Z. Data Mining Based on Clonal Selection Wavelet Network[C]. Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing,2007:665-669.
[25]CHU N J S, LIM J P, JUNG H K. Optimal design of synchronous motor with parameter correction using immune algorithm[J]. IEEE T ransactions on Energ y Co nversion,1999,14(3):610-615.
[26]COELLO C C A,VELD-HUIZEN D A van,LAMONT G B,et al. Evolutionary algorithms for solving multi-objective problems[M].Berlin:Springer,2002,
[27]Cutello V., Narzisi G., Nicosia G., and Pavone M. An Immunological Algorithm for Global Numerical Optimization[C]. Artificial Evolution:7th Int. Conference, Evolution Artificielle, EA 2005, October 26-28, Lille, France, Springer,2005,3871:284-295.
[28]Cutello V., Nicosia G. and Pavone M. Real Coded Clonal Selection Algorithm for Unconstrained Global Optimization using a Hybrid Inversely Proportional Hyper mutation Operator[C]. The 21st Annual ACM Symposium on Applied Computing,2006,2:950-954.
[29]D.S.Turner, J.A.Richardson. Bridge Management System Data Needs and Data Collection Characteristics of Bridge Management Systems [C]. Proc.,7th Conf.on Bridge Mgmt.,Transp. National Academy Press,1994:5-15.
[30]Dabrowski J. and Kubale M.,2008. Computer Experiments with a Parallel Clonal Selection Algorithm for the Graph Coloring Problem. IEEE International Symposium on Parallel and Distributed Processing (IPDPS 2008),14-18 April, Miami, FL, USA, pp.1-6.
[31]Danzhen G., Qian A., and Chen C. The Application of Artificial Immune Network in Load Classification[C]. Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies,6-9 April, Nanjing, China,2008:1394-1398.
[32]Dasgupta, S., Vankayala, V.K.. Developing Real Time Business Intelligence Systems[C]. In:1st Annual IEEE Systems Conference,2007.
[33]DAS S, NATARAJAN B, STEVENS D, et al,. Multi-objective and constrained optimization for DS-CDMA code design based on the clonal selection principle [J].Applied Soft Computing,2008, 8 (1):788-797.
[34]DE CAST RO L N, TIMMI S J. Artificial immune system:a new computational intelligence approach [M]. Berlin:Spinger-Ver lag,2002.
[35]Deb K, Rao U V, Karthik S. Dynamic Multi-objective Optimizationand Decision Making Using Modified NSGA-Ⅱ:A Case Study onHydro-thermal Power SchedulingfC]. Proc. of Conference onEvolutionary Multi-criterion Optimization. Berlin, Germany:Springer,2007:803-817.
[36]DEB K,AGRAWALS,PRATAP A,et al. Afast elitist non-domina-ted sorting genetic algorithm for multi-objective optimization[C].Proc of the6th International Conference on Parallel Problem Solving from Nature. London:Springer-Verlag,2000:849-858.
[37]Dioubate Mamady I基于进化免疫系统理论的多机器人协作和机器人目标探索研究[D].中南大学,2009.
[38]Dirk Pieter van Donk etc. From organising as projects to projects as organizations[J]. International Journal of Project Management.2008,26.
[39]Eom SB. Decision support systems [C].10th International Encyc10paedia of Business and Management,2001.
[40]Farmer JD, Packard NH, Perelson AS. The immune system, adaptation and machine learning [J].Physica D:Nonlinear Phenomena,1986,22 (1-3):187-204.
[41]Federal Highway Administration (FHWA).Asset Management Preserving a$1 Trillion Investment. FOCUS[R]. FHWA,2000:1-2.
[42]Flyvbjerg, B., M.K. Skamris Holm and S.L. Buhl. How (in) accurate are demand forecasts in public works projects? The case of transportation [J]. Journal of the American Planning Association,2005, 71 (2):131-177.
[43]FORREST S, HOFM EYR S A, SOM AYAJI A. Computer immunology [J]. Communications of the ACM,1997,40(10):88-96.
[44]Foster I, Kesselman C. The grid:blueprint for a new computing infrastructure [M].San Francisco: Morgan Kaufmann Publishers,1998.
[45]G.Thomas, W.Fernandez. Success in IT projects:a matter of definition [J]. International Journal of Project Management.2008,26:733-742.
[46]G.W.Hoffmann. An Neural Network Model Based on the Analogy with the Immune System [J]. Eur. J. Immunol,1986 (5):638-647
[47]GASPAR, COLLARD P. Two models of immunization for time inde-pendent optimization[C].Proc of IEEE International Conference on Systems, Man, and Cybernetics.2000:113-118.
[48]Geraldi J.G., Lee-Kelley.L., Kutsch E. The Titanic Sunk, So What-Project Manager Response to Unexpected Events [J]. International Journal of Project Management,2009, (10):8.
[49]Gigerenzer. The Psychology of Good Judgment:Frequency Formats and Simple Algorithms [J]. Medical Decision Making,1996,16(3):273-280.
[50]GOLDBERG G E, SEGRESTP. Finite Markov chain analysis of ge-netic algorithms[C]. Proc of the 2nd International Conference onn Genetic Algorithms on Genetic Algorithms and Their Application. Hillsdale:L Erlbaum Associates Inc,1987:1-8.
[51]H.Hawk, E.P.Small. The BRIDGIT Bridge Management System [J]. Structural Engineering International.1998,8(4):309-314.
[52]Hitoshi Furuta, Jianhong He, Eiichi Watanabe. A Fuzzy Expert System for Damage Assessment Using Genetic Algorithms and Neural Networks [J].Journal of Computing in Civil Engineering. 1996,11.
[53]Hoffmann G W. A neural network model based on the analogy with the immune system [J].Journal of Theoretical Biology,1986,122 (1):33-67.
[54]Hunt J et al,. Learning Using an Artificial Immune System [J]. Journal of Network and CornPuter Application,1996,19:189-212.
[55]Hwang, C. L, and Abu Syed Md Masud. Multiple Objective Decision Making, Methods And Applications:a State-of-the-art Survey [M]. Berlin:Springer-Verlag,1979.
[56]Ichizou Mikami, Shigenori Tanaka and Akira Kurachi.Expert System with Learning Ability for Retrofitting Steel Bridges [J]. Journal of Computing in Civil Engineering. ASCE,1994,8(1).
[57]Igawa K. and Ohashi H. A Negative Selection Algorithm for Classification and Reduction of the Noise Effect [J]. Appl. Soft Comput. J.2008.
[58]Ignizio, James P. Comments on the special volume:Mathematical programming with multiple objectives [J]. Computers and Operations Research,1981,8(4):355-356.
[59]J. Sullivan,R. Beach. Improving project outcomes through operational reliability:a conceptual model[J]. International Journal of Project Management.2009, vol.27:765-775.
[60]J.D. Farmer, N.H. Packard, and A. Perelson. The Immune System, Adaption, and Machine Learning [J].Physica D,1986 (22):187-204.
[61]J.de Brito, F.A.Branco, P.Thoft-Christensen, J.D.Srensen. An Expert System for Concrete Bridge Management [J]. Engineering Structure.1997,19(7).
[62]J.V.Chelsom, A.C.Payne and L.R.P. Reavill, Management for Engineers, Scientists and Technologists [M]. John Wiley & Sons,2005.
[63]James M Lyneis, David N. Ford. System dynamics applied to project management:a survey, assessment, and directions for future research [J].System Dynamics Review,2007, Vol.23 (2-3):157-189.
[64]Jarosz, P., Burczyski, T.:Coupling of immune algorithms and game theory in multiobjective optimization [J]. Artifical Intelligence and Soft Computing,2010,500-507.
[65]Jerne N K. Toward a network theory of the immune system [J].Annual of Immunology,1974,125 (2):373-389.
[66]JIAO Licheng, DU Haifeng. Development and pro spectof the artificial immune system [J]. Acta Electr onica Sinica,2003,31(10):1540-1548.
[67]JIAO Li-cheng,GONG Mao-guo,SHANG Rong-hua,et al. Clonal selection with immune dominance and energy based multiobjective op-timization[C].Proc of the3rd International Conference on Evolu-tionary Multi-Criterion Optimization. Berlin:Springer,2005:474-489.
[68]John Bennett. International Construction Project Management general theory and practice [M]. Butterworth-Heinemann Ltd,1991.
[69]Jon Timmis, Mark Neal, John Hunt. An artificial immune system for data analysis [J].Biosystems, 2000,55(1-3):143-150.
[70]Joshua D Knowles, David W Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy [J].Evolutionary Computation,2000,8 (2):149-172.
[71]Kalmanje KrishnaKumar, James Neidhoefer. Immunised Neurocontrol [J].Expert Systems With Applications,1997,13 (3):201-214.
[72]KIMJ, BENTLEY P J. A model of gene library evolution in the dy-namic clonal selection algorithm [C].Proc of the 1st International Conference on Artificial Immune Systems.2002:9-11.
[73]Knight T. and Timmis J. A Multi-Layered Immune Inspired Approach to Data Mining[C]. Proceedings of the 4th International Conference on Recent Advances in Soft Computing, 2002:266-271.
[74]Lars Taxen, Joakim Lilliesk Id. Images as action instruments in complex projects[J].International Journal of Project Management,2008,26 (5):527-536.
[75]Lee,Sang-Ho; Jeong,Yeon-Suk.A System Integration Framework through Development of ISO 10303-Based Product Model for Steel Bridges [J]. Automation in Construction.2006,15(2): 212-228.
[76]Leijten, M. Big Dig:Een halve eeuw Central Artery in Boston [J]. H. de Bruijn, G.R. Teisman, J. Edelenbos and W,2004.
[77]Levin, M.S.:Composite Systems Decisions (Decision Engineering) [M]. Springer, NewYork, Inc. 2006
[78]Lin, C. H., Chen, C. S., Wu, C. J.,& Kang, M. S. Application of immune algorithm to optimal switching operation for distribution-loss minimisation and loading balance[C]. IEE Proceedings: Generation, Transmission and Distribution,2003,150(2):183-189.
[79]Linkov I, Figueira JR, Levchenko A, Tervonen T, Tkachuk A, Satterstrom FK, Seager TP. A multi-criteria decision analysis approach for prioritization of performance metrics U.S. government performance and response act and oil spill response [J]. Managing Critical Infrastructure Risks,2007:261-298.
[80]LIU Y, XIAO R B. Optimal synthesis of mechanisms for path generatio nusing refined numerical representation based model and AIS based sear ching method [J].ASME Jo ur nal o f Mechanical Design,2005,127 (4):688-691.
[81]LU Bin, JIAO Li-cheng, DU Hai-feng, et al. IFMOA:immune for-getting multiobjective optimization algorithm[C]. Proc of the 1st In-ternational Conference on Advances in Natural Computation.2005:399-408.
[82]M. Hallgren, T. Wilson. The nature and management of crises in construction projects: projects-as-practice observations [J].International Journal of Project Management.2008, vol. 26:830-838.
[83]M.K.S derqvist, M.Veijola. The Finnish Bridge Management System [J].Structural Engineering International.1998,8(4):315-319.
[84]M.Liu, D.M.Frangopol.Optimizing Bridge Network Maintenance Management under Uncertainty with Conflicting Criteria:Life-Cycle Maintenance, Failure, and User Costs [J]. Journal of Structural Engineering.2006,132(11):1835-1845.
[85]M.R.Gholamian, S.M.T.FatemiGhomi, M.Ghazanfari.A hyrid intelligent system for multi-objective decision making Problems[J].ComPuters&Industrial Engineering,2006(51):26-43
[86]MacCormick. Judgment Analysis of Surgeons 'Prioritization of Patients for Elective General Surgery [J]. Medical Decision Making,2006,26(3):255-264.
[87]N. Lesca, ML. Caron-Fasan. Strategic scanning project failure and abandonment factors:lessons learned [J]. European Journal of Information System.2008,17:371-86.
[88]Nanas, N., Uren, V., de Roeck, A., Domingue, J.:Multi-topic information filtering with a single user profile[C]. In:3rd Hellenic Conference on Artificial Intelligence,2004:400-409.
[89]Neal M. An Artificial Immune System for Continuous Analysis of Time-Varying Data[C]. In Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS),2002, 1:76-85.
[90]P.D.Thompson, et al. The PONTIS Bridge Management System [J].Structural Engineering International.1998,8(4):303-308.
[91]ParkD, KandelA, Langholz G Genetie-Based New Fuzzy Reasoning Models with Application to Fuzzy Control[J].IEEE Transon SMC,1994,24(1):39-7.
[92]Peter E D Love, Zahir Irani, Craig Standing, Chad Lin, Janice M Burn. The Enigma of Evaluation: Benefits,Costs and Risks of FT in Australian Small Me-dium-sized Enterprises[J].Information& Manage- ment,2005, (42).
[93]R.Deaton, M.Garzon, J.A.Rose, R.C.Murphy, S.E.Stevens, D.R.Franceschetti. DNABased Artificial immune System for Self-Nonself Discrimination [J]. Proceedings of the 1997 IEEE International Conference on System, Orlando, Florida.1997.8
[94]Rechtin, E.:Systems architecting of organizations:Why eagles can't swim [M]. CRC Press,1999.
[95]S. Forrest, A. S. Perelson. Genetic algorithms and the immune system [M]. In H. Schwefeland R. Maenner, editors, Parallel Problem Solving from Nature, Berlin,1991.
[96]S. Forrest, S. Hofineyr. Computer Immunology [J]. Communications of the ACM,1997, 40(10):88-96.
[97]S. Hofmeyr, S. Forrest. Arichitecture for an Artificial Immune System [J]. Evolutionary Computation Journal,1999,7(1):45-68.
[98]S.Forrest, A.S.Perelson, L.Allen, R.Cherukuri. Self-Nonself Discrimination in a Computer [J]. Proceedings of IEEE Symposium on Research in Security and Privacy, Oakland.1994.5
[99]Sara A. McComb, Stephen G. Green, W. Dale Compton. Team flexibility's relationship to staffing and performance in complex projects:An empirical analysis [J].Journal of Engineering and Technology Management,2007, Volume 24 (Issue 4):Pages293-313.
[100]Shenhar A J,Dvir D. Project Management Research-the Chal-lenge and Opportunity[J].Project Management Journal,2007,38 (2):93-99.
[101]Simon Austin, Andrew Newton, John Steele, Paul Wasket. Modeling and managing project complexity [J].International Journal of Project Management,2002, (20):191-198.
[102]Smith G F. Managerial problem identification [J]. Omega,1989,17(1):27-36.
[103]Soderholm A. Project Management of Unexpected Events [J].International Journal of Project Management,2008,26 (1):80-86.
[104]Somayaji, Anil. Immunology, diversity, and homeostasis:The past and future of biologically inspired computer defenses [J]. Information Security Technical Report,2007,12(4):228-234.
[105]Srinivas N, Kalyanmoy D. Multiobjective optimization using nondominated sorting in genetic algorithms [J].Evolutionary Computation,1994,2 (3):221-248.
[106]Stanley Zionts, Jyrki Wallenius. An Interactive Programming Method For Solving The Multiple Criteria Problem [J]. Management Science,1976,22(6):652.
[107]Sun-Kon Kim,Kyung-Sik CHO,Jong-Hwa Kim.Development of Computerized Management System for Cable-Supported Bridge[C].IABSE Conference,Seoul,2001.
[108]TAN K C,GOHC K,MAMUN A A,et al. An evolutionary artificial immune system for multi-objective optimization [J].Artificial Intelli-gence Review,2002,187 (2):371-392.
[109]TMMIS J, KNIGHT T. Data mining:a heuristic approach [M]. Hershey:Idea Publishing Group, 2001.
[110]Timmis. J. J. Hunt, D. Cooke, M. Neal, C. King. Jisys:the development of an artificialimmune System for real world applications [M]. Springer-Verlag.1999.
[111]Turoff M, Chumer M, Van de Walle B, Yao X. The design of a dynamic emergency response management information system [J]. J Inf Tech Theor,2004, Appl 5(4):1-36
[112]Van Den Eede G, Muhren W, Smals R, Van de Walle B.Incident response information systems capability:the role of the DERMIS design premises[C]. In:Van de Walle B, Turoff M (eds) Proceedings of the 3rd international conference on information systems for crisis response and management ISCRAM 2006,251-261
[113]Van Noortwijk, J.M. A survey of the application of gamma processes in maintenance [J]. Reliability Engineering and System Safety,2009,94(1):2-21.
[114]Varela F.J., Stewart J. Dynamics of a Class of Immune Network, Global Stability ofldiotype Interactions [J]. J. theoretical Biology,1990,144.
[115]Varela, F.J., Coutinho, A. Second generation immune network [J]. Immunology Today 12,1991: 159-166.
[116]Volkema.R.J. Problem formulation in planning and design [J]. Management Seienee,1983, 29(6):639-652.
[117]WANG Xiao-lan,MAHFOUF M. ACSAMO:an adaptive multiobjec-tive optimization algorithm using the clonal selection principle[C].Proc of the2nd European Symposium on Nature inspired Smart Infor-mation Systems.2006.
[118]WATKINS A, BOGGESS L. A new classifier based on resour celimited artificial immune systems [C]. Proceedings of Congress on Evolutionary Computation[C]. Washington,2002.
[119]Whitty, S.J., Maylor, H. And then came Complex Project Management (revised) [J].International Journal of Project Management,2009, (4).
[120]XIAN Bin, CAO Hong-qiao, XU Yan-wu. Negative selection based immune optimization [J].Advances in Engineering Software Elsevier Science,2007,38 (10):649-658.
[121]XIAO R B, TAO Z W, LIU Y. Isomor phism ident ification of kinematic chains using novel evolutionary approaches [J]. ASME Journal of Computing and Information Science,2005,5(1): 18-24.
[122]Yang Lunbiao. Make an approach to the optimal solution of fuzzy non-linear programming[C].AMSE conference signal and systems.Dalian,1989,1 (9):73-81.
[123]YAO Changfeng.Research on Multi-objective Fuzzy Decision-making Method Based on GA [J]. Journal of System Simulation,2005,17(10):2341-2348.
[124]Z.Tang, Yamaguchi T, Tashima K et al. Multiple-valued Immune Network Model and Its Simualtions [J]. In:Proc 27th Int Symposium on Multiple-valued Logic. Autigonish, Canada, 1997:233-238.
[125]Zeng J., Li T., Liu X., Liu C., Peng L. and Sun F. A Feedback Negative Selection Algorithm to Anomaly Detection[C]. Third International Conference on Natural Computation,2007,3:604-608.
[126]Zitzler E, Thiele L. An evolutionary algorithm for multiobjective optimization:The strength Pareto approach[R].Technical Report 43, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH). Zurich, Switzerland:1998.
[127]白广忱.系统可意性多目标优化的满意度[J].系统工程与电了技术,2000,22(8)：85-87.
[128]曾先彬,刘克胜,王煦法.基于免疫进化规划的多层前馈网络设计[J].软件学报,1999, 10(11)：1180-1184.
[129]曹先彬,郑振,刘克胜,等.免疫进化策略及其在二次布局求解中的应用[J].计算机工程,2000,26(3)：1-10.
[130]岑健.基于人工免疫系统的机组复合故障诊断技术研究[D].华南理工大学,2010.
[131]陈婕.基于免疫算法的演化多目标优化方法研究[D].武汉理工大学,2010.
[132]陈伟锋.大规模复杂过程系统的高性能优化理论与方法研究[D].浙江大学,2011.
[133]陈文清.基于免疫机理的水泥生产工艺故障智能诊断方法研究[D].华中科技大学,2011.
[134]丁永生,任立红.人工免疫系统：理论和应用[J].模式识别与人工智能,2000,13(1)：52-59.
[135]窦振中.模糊逻辑控制技术及其应用[M].北京：北京航空航天大学出版社,1994
[136]段鹰.复杂生产系统决策若干关键技术研究[D].重庆大学,2009.
[137]樊治平,尤天慧等.属性权重信息不完全的区间数多属性决策方法[JJ.东北大学学报,2005,8：798—800.
[138]冯英浚.关于多目标最优化问题的Fuzzy解[J].科学通报,1981,17：1028-1030.
[139]高萍.基于可靠性分析的复杂设备预防性维修决策研究[D].清华大学,2008.
[140]龚固丰.人工免疫系统在非线性系统辨识与预测控制中的应用研究[D].湖南大学,2009.
[141]勾轶.基于免疫算法和多传感器信息融合的电机故障综合诊断方法研究[D].沈阳工业大学,2010.
[142]顾连胜.大型工程投资决策中风险管理的理论与应用研究[D].西安建筑科技大学,2004
[143]郭振华.大型项目建筑工程一切险承保风险评估方法[J].同济大学学报(自然科学版),2005(7),985-989.
[144]韩旭明.若干改进的人工免疫算法及其在大气环境领域中的应用[D].吉林大学,2010.
[145]何磊,戴冠中.一种基于模糊遗传算法的隶属函数自学习方法研究[J].西北工业大学学报,1997,2：88-92.
[146]胡秦生.模糊多目标规划最小隶属度偏差法[J].运筹学杂志,1995,11：38-42.
[147]胡宇博.基于多目标免疫进化算法的动态车辆路径优化研究[D].郑州大学,2009.
[148]胡志华.基于免疫系统的协同进化机制及其应用研究[D].东华大学,2009.
[149]黄席樾,张著洪,何传江,等.现代智能算法理论及应用[M].北京：科学出版社,2005.
[150]蒋静坪.计算机实时控制[M],浙江大学1川饭社,1992年4月第一版.
[151]焦李成,杜海峰.人工免疫系统进展与展望[J].电子学报,2003,31(10)：1540-1548.
[152]雷佳等,基于遗传算法的模型参考自适应控制[J].控制理论与应用,1998,15(3)：446,-468.
[153]李广强,滕弘飞,霍军周.并行混合免疫算法及其在布局设计中的应用[J].机械工程学报,2003,39(6)：79-85.
[154]李擎,郑德玲等-种新的模糊遗传算法[J].北京科技人学学报,2001,2：86-89
[155]李群育.免疫优化算法及其在投资组合中的应用研究[D].重庆大学,2004.
[156]李荣钧.模糊多准则决策理论与应用[M].北京：科学出版社,2002.
[157]李润心.基于免疫克隆选择的维数缩减及其应用[D].西安电子科技大学,2010.
[158]李涛.基于免疫的网络监控模型[J].计算机学报,2006,29(9)：1515-1522.
[159]李向华.基于人工免疫系统的增量聚类算法及其优化与应用的研究[D].吉林大学,2009.
[160]李学全,李辉.带权值的模糊多目标线性规划[J].经济数学,2003,4：81-85
[161]李中.基于人工免疫系统的电力变压器故障诊断技术研究[D].华北电力大学(河北),2010.
[162]梁意文,潘海军,康立山.免疫识别器构造的多级演化[J].小型微型计算机系统,2002,23(4)：441-443.
[163]林峰等,联姻遗传算法在CSTR系统中的应用[J].控制与决策,2001,16(4)：457～460.
[164]刘芳.基于免疫多目标优化的否定选择算法[D].西安电子科技大学,2011.
[165]刘丽珏.免疫进化算法及其在多机器人协作中的应用研究[D].中南大学,2008.
[166]刘妹琴等.实时遗传算法在位置伺服系统中的应用[J].中国电机土程学报,2000,20(2)：5—9.
[167]刘若辰,杜海峰,焦李成.免疫多克隆策略[J].计算机研究与发展,2004,41(4)：571-576.
[168]刘星宝.人工免疫系统的若干关键问题研究[D].中南大学,2010.
[169]刘玉.基于改进免疫算法的多目标优化研究[D].哈尔滨工程大学,2011.
[170]鲁慧君.重大项目的决策机制探讨[J].中国工程咨询,2004(9),7-8.
[171]栾林林.基于人工免疫算法的故障诊断方法研究[D].华东理工大学,2012.
[172]马慧敏.基于免疫理论的非常规突发事件应急管理主动免疫系统研究[D].武汉理工大学,2010.
[173]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].东北大学,2008.
[174]马丽仪,邱菀华,杨亚琴.大型复杂项目风险建模与熵决策[J].北京航空航天大学学报,2010,02：61-64.
[175]门文周,侯渡舟等.工程项目投资决策中的风险评价研究[J].西安建筑科技大学学报(白然科学版),2004(9),337-344.
[176]莫宏伟,金鸿章.人工免疫系统一个新兴的交叉学科[J].计算机工程与科学,2004,26(5)：70-73.
[177]牛慧峰.免疫机理与支持向量机复合的故障诊断理论及试验研究[D].燕山大学,2009.
[178]裴星鑫.用于组合优化的人工免疫算法设计与分析[D].中国科学技术大学,2010.
[179]彭维,黄辉先,徐建伟,李密青.一种基于记忆克隆选择的多目标免疫算法[J].计算机工程与应用,2008,16：60-63.
[180]钱淑渠,张著洪.动态多目标免疫优化算法及性能测试研究[J].智能系统学报,2007,05：72-81.
[181]饶从军,肖新平.多目标决策问题的模糊数学解法[J].武汉理工大学学报,2006,8：700—703.
[182]孙凯.基于免疫算法与分散搜索的钢铁生产调度研究[D].上海交通大学,2009.
[183]孙增析.智能控制理论与技术[M].北京：清华人学出版,1997,4.
[184]汤炎非,杨青.可行性研究与投资决策口伽.武汉：武汉大学出版社,1998.5.
[185]田玉玲.多层免疫模型及其在故障诊断中的应用研究[D].太原理工大学,2009.
[186]王洪礼,郭树起,谭欣.生化系统中时间延迟与非线性振荡研究[J].化学反应工程与工艺,1999,(04).
[187]王坚强.信息不完全的多准则决策的SIR方法[J].系统工程与电子技术,2004.26(9)：1205—1208.
[188]王炼红.人工免疫优化与分类算法及其应用研究[D].湖南大学,2009.
[189]王敏蔚.基于改进免疫算法的机组组合优化[D].浙江大学,2011.
[190]王润良,郑晓齐,孙建平.技术复杂性及其对组织结构的影响[J].科学学研究,2001,(03)：29-33.
[191]王卫民.基于人工免疫系统的否定选择算法改进相关研究[D].电子科技大学,2008.
[192]王小林.项目投资群体决策模型研究[M].北京：中国农业出版社,2005.6
[193]王晓睿.隧道软弱围岩大变形监控及免疫智能反分析[D].华中科技大学,2009.
[194]王亚奇.多传播因素的复杂网络病毒传播及免疫策略研究[D].南京邮电大学,2011.
[195]伍进伟.工程建设项目投资管理与决策分析[J].工程建设与设计,2003(4),29-31.
[196]席酉民.大型工程决策[M].贵州：贵州人民出版社,1988.3.
[197]肖人彬,王磊.人工免疫系统：原理、模型、分析及展望[J].计算机学报,2002,25(12)：1281-1293.
[198]肖人彬,周济.决策问题结构化的一个测度[J].决策与决策支持系统,1994,4(1)：64-68.
[199]肖人彬.基于免疫计算的机构轨迹综合[J].计算机辅助设计与图形学学报,2004,16(6)：812-818.
[200]萧毅鸿.基于本体的复杂决策任务表示方法与求解技术研究[D].南京大学,2011.
[201]徐立芳.免疫克隆选择算法应用研究[D].哈尔滨工程大学,2008.
[202]徐锐.人工免疫算法优化及其应用研究[D].上海大学,2009.
[203]徐泽水一种交互式多目标决策方法[J].系统工程理论与实践,2002(2)：104-108.
[204]许力CSTR系统的基于CMAC(?)申经网络的学习控制研究[J].控制与决策1992,7(2)：131-136.
[205]续欣莹.基于免疫应答原理的人工免疫算法及其应用[D].太原理工大学,2009.
[206]杨纶标.多目标非线性规划的Fuzzy解的研究[J].华南理工大学学报,1993,2.
[207]叶洪涛.基于免疫算法的污水处理系统预测及优化控制研究[D].华南理工大学,2011.
[208]叶剑锋.基于免疫优化算法的阵列天线综合的研究[D].哈尔滨工程大学,2009.
[209]尤勇,盛万兴,王孙安.基于人工免疫网络的短期负荷预测模型[J].中国电机工程学报,2003(3)：26-29.
[210]袁桂丽.人工免疫系统及其在电站控制中的应用研究[D].华北电力大学(北京),2010.
[211]原霞飞.基于人工免疫算法的电子商务推荐系统的研究[D].山东师范大学,2011.
[212]张仕彬,黄文杰.大型工程项目的投保决策方法[J].建筑管理现代化,2004(1),6-9.
[213]张欣莉.基于目标满意度的交互式多目标决策改进方法[J].系统工程,2004,22(9)：10-12.
[214]郑中义.船舶自动避碰决策系统的研究[D].大连海事大学,2002.
[215]钟成勋,刘树云.项目投资决策通论[M].呼和浩特：内蒙古大学出版社,1992.10
[216]朱道海,瞿良华.大型工程项目决策分析与环境因素[J].上海大学学报(自然科学版),2001(8),342-344.
[217]朱虎明.基于集群计算的免疫优化算法及其应用研究[D].西安电子科技大学,2010.
[218]朱玉.面向瓦斯突出预测的人工免疫算法与模型研究[D].中国矿业大学,2009.
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