群体智能新方法在优化和模拟中的研究
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摘要
群体智能是基于自治、非中央控制系统的聚合行为的人工智能。随着对复杂系统的深入研究,传统的“从上而下”的研究方法遇到了很多的困难。一种新的研究方法---群体智能被广泛应用在复杂系统的优化、模拟、仿真等领域。群体智能方法两个重要的研究领域是优化模型和基于agent的模拟上。在优化模型中比较著名的是蚂蚁算法和粒子群算法。人工社会系统是基于agent的模拟的重要组成部分。本文主要研究内容如下:
1)介绍了群体智能方法的起源及基本方法学。
2)介绍了基于群体智能的优化算法,其中包括蚂蚁算法,粒子群算法和基于Agent的优化模型。
3)介绍了群体智能方法在人工社会系统中的应用。基于agent的建模方法在人工社会系统中得到了广泛的应用。同时,群体智能方法提供了一种研究社会系统的新方法—模拟。
4)引入一种特殊的算子以使粒子群算法可以用于求解旅行商问题。
5)使用多Agent模型求解多目标优化问题。在模型中,每个agent都有自己的信念和状态,通过信息的交流和学习自适应的搜索解空间。每个agent都有不同的偏好来优化不同的目标。
6)研究了基于agent的拍卖市场。提出了一个包括生产过程的经济模型,研究了在这个模型中一些经济现象。
7)使用元胞自动机模型建立的微观交通模型研究交通信号控制策略的性能,研究了不确定交通信息下各种控制算法的效果,同时研究了双环路中同步控制策略的特性。
本文的研究结果丰富了群体智能方法在优化和模拟方面的内容,加深了对群体智能方法的认识,有一定的理论意义和应用价值,为进一步的研究提供帮助。
Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems. It is inspired by nature swarm behaviors. The examples of nature SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. The traditional“up-down”methods encounter lots of handicaps in the research of complex systems. A new method, swarm intelligence which is a“bottom-top”research method, is exploited widely in optimization and simulation of complex systems now. Swarm intelligence technology is particularly attractive because it is simple, effective and robust. Swarm intelligence focuses on self-organized, decentralized methods. The swarm systems typically are made up of population of simple agents which interact with each other and environment. The macrostructures emerge from the interactions and operations of agents despite the simple micro rules.
There are many optimization models based on SI, and particle swarm optimization algorithm is a typical one. It is inspired by the social behavior of bird flocking. In this algorithm, every particle will modify its search direction and speed according to the best swarm solution and its own best solution. Following this rule, the swarm can search the solution space effectively. The micro-structure of PSO algorithm is simple, but the macro searching behavior is emerging from it based on inter-operations of particles.
The traditional macro models cannot explain many social phenomena clearly. In such system, there are lot of agents which have interactions and inter-influences, e.g. traffic systems and economic systems. The micro-models based on swarm intelligence approach have made great improvements in recent years. Most of these models focus on sociology and economics. The agent-based computational economics is a classical one. SI presents a different perspective from micro level. Swarm intelligence techniques have lots of advantages to simulate these systems. In simulations of economic systems, swarm intelligence is used to study and validate economic hypotheses that are hard for traditional approaches.
The work of this dissertation focuses on optimization and simulation applying SI approaches. There are two important issues in the SI optimization model: How to map agents to solution space? How agents cooperate to improve the searching efficiency.
The two main parts of the work on optimization are listed below:
1) A new PSO algorithm is presented for solving travel salesman problem. Swap operator is presented as“speed”to change the current solution to a new one. The distance of two solutions can also be denoted as“swap operator”. In this way, particle swarm optimization can be used in discrete domain.
2) An agent-based algorithm is presented for solving multi-objective optimization problems. In multi-objective problems, the key is how to mediate different objectives. Agents with different intents are used to search the solution space. Agents can exchange information with their neighbors in the searching process to improve the local searching efficiency. One evolutional operator is also employed. The experimental results show this method is effective.
In simulation area, the following subjects are studied:
1) We research CDA market. Some well-known strategies are implemented to study their performances.
2) Agent-based simulation on pricing in the barter market. The continuous double auction model in which the supply and demand are fixed is used to explore the pricing process of agents. In this dissertation, a novel and interesting agent model is proposed to explore some properties of a typical and meaningful phenomenon in economics, pricing.
In our model, not only auction is included, but also production. We analyse the pricing procedure. We find the price is stable and the variance of price is small. There are some macro phenomena emerging from this model, e.g. the business man, the social labor allocation. And then the impact of transaction capability is researched.
3) Traffic signal simulation and research. A traffic system is simulated on cellular automata. Because of the complexity of environment, the traffic information always contains some noises. In this model, some traffic signal methods are tested with noisy traffic information. We find the complicated intelligent strategies are worse when there are noises in traffic information. The light control methods depend on the road structure and the traffic density severely. Traffic light synchronization method is studied on double ring road structure with five different densities. With one of the densities, the traffic light synchronization method has some problems. An improved method is presented to handle them, and the results are promising.
In the end, the summaries and further works are presented.
1)介绍了群体智能方法的起源及基本方法学。
2)介绍了基于群体智能的优化算法,其中包括蚂蚁算法,粒子群算法和基于Agent的优化模型。
3)介绍了群体智能方法在人工社会系统中的应用。基于agent的建模方法在人工社会系统中得到了广泛的应用。同时,群体智能方法提供了一种研究社会系统的新方法—模拟。
4)引入一种特殊的算子以使粒子群算法可以用于求解旅行商问题。
5)使用多Agent模型求解多目标优化问题。在模型中,每个agent都有自己的信念和状态,通过信息的交流和学习自适应的搜索解空间。每个agent都有不同的偏好来优化不同的目标。
6)研究了基于agent的拍卖市场。提出了一个包括生产过程的经济模型,研究了在这个模型中一些经济现象。
7)使用元胞自动机模型建立的微观交通模型研究交通信号控制策略的性能,研究了不确定交通信息下各种控制算法的效果,同时研究了双环路中同步控制策略的特性。
本文的研究结果丰富了群体智能方法在优化和模拟方面的内容,加深了对群体智能方法的认识,有一定的理论意义和应用价值,为进一步的研究提供帮助。
Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems. It is inspired by nature swarm behaviors. The examples of nature SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. The traditional“up-down”methods encounter lots of handicaps in the research of complex systems. A new method, swarm intelligence which is a“bottom-top”research method, is exploited widely in optimization and simulation of complex systems now. Swarm intelligence technology is particularly attractive because it is simple, effective and robust. Swarm intelligence focuses on self-organized, decentralized methods. The swarm systems typically are made up of population of simple agents which interact with each other and environment. The macrostructures emerge from the interactions and operations of agents despite the simple micro rules.
There are many optimization models based on SI, and particle swarm optimization algorithm is a typical one. It is inspired by the social behavior of bird flocking. In this algorithm, every particle will modify its search direction and speed according to the best swarm solution and its own best solution. Following this rule, the swarm can search the solution space effectively. The micro-structure of PSO algorithm is simple, but the macro searching behavior is emerging from it based on inter-operations of particles.
The traditional macro models cannot explain many social phenomena clearly. In such system, there are lot of agents which have interactions and inter-influences, e.g. traffic systems and economic systems. The micro-models based on swarm intelligence approach have made great improvements in recent years. Most of these models focus on sociology and economics. The agent-based computational economics is a classical one. SI presents a different perspective from micro level. Swarm intelligence techniques have lots of advantages to simulate these systems. In simulations of economic systems, swarm intelligence is used to study and validate economic hypotheses that are hard for traditional approaches.
The work of this dissertation focuses on optimization and simulation applying SI approaches. There are two important issues in the SI optimization model: How to map agents to solution space? How agents cooperate to improve the searching efficiency.
The two main parts of the work on optimization are listed below:
1) A new PSO algorithm is presented for solving travel salesman problem. Swap operator is presented as“speed”to change the current solution to a new one. The distance of two solutions can also be denoted as“swap operator”. In this way, particle swarm optimization can be used in discrete domain.
2) An agent-based algorithm is presented for solving multi-objective optimization problems. In multi-objective problems, the key is how to mediate different objectives. Agents with different intents are used to search the solution space. Agents can exchange information with their neighbors in the searching process to improve the local searching efficiency. One evolutional operator is also employed. The experimental results show this method is effective.
In simulation area, the following subjects are studied:
1) We research CDA market. Some well-known strategies are implemented to study their performances.
2) Agent-based simulation on pricing in the barter market. The continuous double auction model in which the supply and demand are fixed is used to explore the pricing process of agents. In this dissertation, a novel and interesting agent model is proposed to explore some properties of a typical and meaningful phenomenon in economics, pricing.
In our model, not only auction is included, but also production. We analyse the pricing procedure. We find the price is stable and the variance of price is small. There are some macro phenomena emerging from this model, e.g. the business man, the social labor allocation. And then the impact of transaction capability is researched.
3) Traffic signal simulation and research. A traffic system is simulated on cellular automata. Because of the complexity of environment, the traffic information always contains some noises. In this model, some traffic signal methods are tested with noisy traffic information. We find the complicated intelligent strategies are worse when there are noises in traffic information. The light control methods depend on the road structure and the traffic density severely. Traffic light synchronization method is studied on double ring road structure with five different densities. With one of the densities, the traffic light synchronization method has some problems. An improved method is presented to handle them, and the results are promising.
In the end, the summaries and further works are presented.
引文
[1] BENI G, WANG J. Swarm Intelligence in Cellular Robotic Systems; proceedings of the Proceed NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, F, 1989 [C].
[2] BONABEAU E, DORIGO M, THERAULAZ G. Swarm Intelligence: From Natural to Artificial Systems [M]. Oxford, 1999.
[3]金吾伦,郭元林.复杂性科学及其演变[J].复杂系统与复杂性科学, 2004, 1(1): 1-5.
[4] LANGTON C. Artificial Life, F, 1987 [C].
[5]贲可荣,张彦铎.人工智能[M].清华大学出版社.
[6] WOLFRAM S. Computation theory of cellular automata [J]. Communications in Mathematical Physics, 1984, 96(1): 15-57.
[7] CULIK K, HURD L P, YU S. Computation theoretic aspects of cellular automata [J]. Physica D 1990, 45(1):357-78.
[8] CONWAY J. The Game of Life [J]. Scientific American, 1970, 223(1): 120-23.
[9] EPSTEIN J M, AXTELL R. Growing Artificial Societies: Social Science from the Bottom Up [M]. Brookings Institution Press, 1996.
[10] DORIGO M, STüTZLE T. Ant Colony Optimization [M]. Mit Press, 2004.
[11] DORIGO M, GAMBARDELLA L M. Ant colony system: a cooperative learning approach to the travelingsalesman problem [J]. Evolutionary Computation, IEEE Transactions on, 1997, 1(1): 53-66.
[12] KENNEDY J, RC E. Particle swarm optimization; proceedings of the IEEE International Conference on Neural Networks, Piscataway, NJ, F, 1995 [C].
[13] EBERHART R, KENNEDY J. A new optimizer using particle swarm theory; proceedings of the Sixth International Symposium on Micro Machine and Human Science, F, 1995 [C].
[14] EBERHART R C, SHI Y. Comparison between Genetic Algorithms and Particle Swarm Optimization [J]. LECTURE NOTES IN COMPUTER SCIENCE, 1998, 611-8.
[15] EBERHART R, SHI Y. Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization; proceedings of the IEEE congress evolutionarycomputation,, San Diego, CA,, F, 2000 [C].
[16] CLERC M, KENNEDY J. The particle swarm-explosion, stability, and convergence in amultidimensional complex space [J]. IEEE Transactions on Evolutionary Computation,, 2002, 6(1): 58-73.
[17] KENNEDY J, EBERHART R. A discrete binary version of the particle swarm algorithm proceedings of the Proceedings of the conference on systems, man and cybernetics, , .Piscataway, New Jersey,, F, 1997 [C].
[18] AL-KAZEMI B, MOHAN C K. Multi-phase generalization of the particle swarm optimizationalgorithm, F, 2002 [C].
[19] AFSHINMANESH F, MARANDI A, RAHIMI-KIAN A. A Novel Binary Particle Swarm Optimization Method Using Artificial Immune System [M]. The International Conference on Computer as a Tool. 2005.
[20] CARLISLE A, DOZIER G. Adapting particle swarm optimization to dynamic environments [M]. International Conference on Artificial Intelligence. 2000: 429-34.
[21] KENNEDY J. The particle swarm: social adaptation of knowledge [M]. IEEE International Conference on Evolutionary Computation. 1997: 303-8.
[22] WEISS G. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence [M]. MIT Press, 1999.
[23]刘大有,杨鲲,陈建中. Agent研究现状与发展趋势[J].软件学报, 2000, 11(3): 315-21.
[24] HAN J, LIU J, CAI Q. From Alife Agents to a Kingdom of N Queens [J]. Intelligent Agent Technology: Systems, Methodologies, and Tools, 1999, 110-20.
[25] LIU J, HAN J, TANG Y Y. Multi-agent oriented constraint satisfaction [J]. Artificial Intelligence, 2002, 136(1): 101-44.
[26] AXELROD R. The Complexity of Cooperation: Agent-based Models of Competition and Collaboration [M]. Princeton University Press, 1997.
[27] SANDHOLM T W, CRITES R H. Multiagent reinforcement learning in the Iterated Prisoner's Dilemma [J]. BioSystems, 1996, 37(1-2): 147-66.
[28] AXELROD R, HAMILTON W D. The evolution of cooperation [J]. Science, 1981, 211(4489): 1390-6.
[29]米传民,刘思峰,江可申.经济学研究的新范式:刍议基于agent的计算经济学[J].山东经济2004, 6(28-33.
[30] TESFATSION L. Agent-based computational economics: modeling economies as complex adaptive systems [J]. Information Sciences, 2003, 149(4): 262-8.
[31]刘晓光,刘晓峰.计算经济学研究新进展——基于Agent的计算经济学透视[J].经济学动态, 2003, 11(58-61.
[32] BELL A M. Reinforcement Learning Rules in a Repeated Game [J]. Computational Economics, 2001, 18(1): 89-110.
[33] DAWID H. Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models [M]. Springer-Verlag New York, Inc. Secaucus, NJ, USA, 1999.
[34] EPSTEIN J M. Learning to Be Thoughtless: Social Norms and Individual Computation [J]. Computational Economics, 2001, 18(1): 9-24.
[35] WAN H A, HUNTER A, DUNNE P. Autonomous Agent Models of Stock Markets [J]. Artificial Intelligence Review, 2002, 17(2): 87-128.
[36] ARTHUR W B, HOLLAND J H, LEBARON B, et al. Asset Pricing Under Endogenous Expectations in an Artificial Stock Market [J]. The economy as an evolving complex system II, 1001(15-44.
[37] LEBARON B. Agent-based computational finance: Suggested readings and early research [J]. Journal of Economic Dynamics and Control, 2000, 24(5-7): 679-702.
[38] CARLEY K M. Computational organizational science and organizational engineering [J]. Simulation Modelling Practice and Theory, 2002, 10(5-7): 253-69.
[39] LUNA F, PERRONE A. Agent-Based Methods in Economics and Finance: Simulations in Swarm [M]. Kluwer Academic Publishers, 2001.
[40] COLELLA V S, KLOPFER E, RESNICK M. Adventures in Modeling: Exploring Complex, Dynamic Systems with StarLogo [J]. 2001,
[41] PREIST C, VAN TOL M. Adaptive agents in a persistent shout double auction [M]. ACM Press New York, NY, USA. 1998: 11-8.
[42] NAGEL K, SCHRECKENBERG M. A cellular automaton model for freeway traffic [J]. Journal de Physique I 1992, 2(1)2221-9.
[43] BIHAM O, MIDDLETON A A, LEVINE D. Self-organization and a dynamical transition in traffic-flow models [J]. Physical Review A, 1992, 46(10): 6124-7.
[44] WIERING M. Multi-Agent Reinforcement Leraning for Traffic Light Control [M]. Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA. 2000: 1151-8.
[45] WANG K P, HUANG L, ZHOU C G, et al. Particle swarm optimization for traveling salesman problem; proceedings of the Second International Conference on Machine Learning and Cybernetics, F, 2003 [C].
[46] JOHNSON D S, MCGEOCH L A. The travelling salesman problem: a case study in local optimization [M]. New York: Wiley and Sons, 1997.
[47] REINELT G. TSPLIB-A Traveling Salesman Problem Library [J]. ORSA Journal on Computing, 1991, 3:376-84.
[48]王康平,郭东伟,周春光.基于信念的多Agent模型解决多目标优化问题[J].计算机研究与发展, 2006, 43(z1): 157-61.
[49] WANG K, ZHOU C, GUO D, et al. Evolutionary Multi-Agent Model with Intent Exchange Solving Multi Objective Optimization Problem; proceedings of the Second International Conference on Complex Systems and Applications-Modeling, Control and Simulations, F, 2007 [C].
[50] COHON J L. Multiobjective Programming and Planning [M]. Dover Publications, 2004.
[51] STEUER R E. Multiple Criteria Optimization: Theory, Computation, and Application [M]. New York: Wiley, 1986.
[52] KOSKI J. Multicriterion Optimization in Structural Design [M]. Storming Media. 1981.
[53] ROSENBERG R S. Simulation of genetic population with bio-chemical properties [D]; University of Michigan, 1967.
[54] SCHAFFER J D. Multiple Objective Optimization with Vector Evaluated Genetic Algorithms [M]. Proceedings of the 1st International Conference on Genetic Algorithms. NJ, USA; Lawrence Erlbaum Associates, Inc. Mahwah. 1985: 93-100.
[55] FONSECA C M, FLEMING P J. Multiobjective optimization and multiple constraint handling withevolutionary algorithms. I. A unified formulation [J]. IEEE Transactions on Systems, Man and Cybernetics, Part A, 1998, 28(1): 26-37.
[56] HORN J, NAFPLIOTIS N, GOLDBERG D E. A niched Pareto genetic algorithm for multiobjective optimization; proceedings of the First IEEE Conference on Evolutionary Computation, Piscataway, F, 1994 [C]. IEEE Service Center.
[57] ZITZLER E, THIELE L. Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach [J]. Evolutionary Computation, IEEE Transactions on, 1999, 3(4): 257-71.
[58] ZITZLER E, LAUMANNS M, THIELE L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm, F, 2001 [C].
[59] SRINIVAS N, DEB K. Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms [J]. Evolutionary Computation, 1994, 2(3): 221-48.
[60] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. Evolutionary Computation, IEEE Transactions on, 2002, 6(2): 182-97.
[61] HU X, EBERHART R. Multiobjective optimization using dynamic neighborhood particleswarm optimization; proceedings of the Congress on Evolutionary Computation, F, 2002 [C].
[62] FIELDSEND J, SINGH S. A multi-objective algorithm based upon particle swarm optimisation; proceedings of the UK Workshop on Computational Intelligence, F, 2002 [C].
[63] COELLO C A C, LECHUGA M S. MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization; proceedings of the Congress on Evolutionary Computation F,2002 [C].
[64] PARSOPOULOS K E, VRAHATIS M N. Particle swarm optimization method in multiobjective problems; proceedings of the 2002 ACM symposium on Applied computing, NY, USA, F, 2002 [C]. ACM New York.
[65] MOSTAGHIM S, TEICH J. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO); proceedings of the 2003 IEEE Swarm Intelligence Symposium, F, 2003 [C].
[66] ABBASS H A. The Self-Adaptive Pareto Differential Evolution Algorithm; proceedings of the Congress on Evolutionary Computation F].
[67] MADAVAN N K, DIV N A S, CENTER N A R, et al. Multiobjective optimization using a Pareto differential evolutionapproach; proceedings of the 2002 Congress on Evolutionary Computation F,2002 [C].
[68] XUE F, SANDERSON A C, GRAVES R J. Pareto-based multi-objective differential evolution; proceedings of the 2003 Congress on Evolutionary Computation F,2003 [C].
[69] BABU B V, JEHAN M M L. Differential evolution for multi-objective optimization; proceedings of the 2003 Congress on Evolutionary Computation F,2003 [C].
[70] HAJELA P, LIN C Y. Genetic search strategies in multicriterion optimal design [J]. Structural and Multidisciplinary Optimization, 1992, 4(2): 99-107.
[71] VELDHUIZEN D A V, LAMONT G B. Multiobjective evolutionary algorithm test suites; proceedings of the 1999 ACM symposium on Applied computing, F, 1999 [C]. ACM Press New York, NY, USA.
[72]张利彪.基于粒子群和微分进化的优化算法研究[D].长春;吉林大学, 2007.
[73] FOSTER J. From simplistic to complex systems in economics [J]. Cambridge Journal of Economics, 2005, 29(6): 873-92.
[74] BONABEAU E. Agent-based modeling: Methods and techniques for simulating human systems [M]. National Acad Sciences. 2002: 7280-7.
[75] AXELROD R. Advancing the Art of Simulation in the Social Sciences [J]. Complexity, 1997, 3(2): 16-22.
[76] GODE D K, SUNDER S. Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality [J]. Journal of Political Economy, 1993, 101(1): 119.
[77] CLIFF D, BRUTEN J. Zero Not Enough: On The Lower Limit of Agent Intelligence For Continuous Double Auction Markets [J]. HP LABORATORIES TECHNICAL REPORT HPL, 1997,
[78] GJERSTAD S, DICKHAUT J. Price Formation in Double Auctions [J]. Games and Economic Behavior, 1998, 22(1): 1-29.
[79] PARK S, DURFEE E H, BIRMINGHAM W P. An adaptive agent bidding strategy based on stochastic modeling; proceedings of the third annual conference on Autonomous Agents, NY, USA, F, 1999 [C]. ACM New York.
[80] TESAURO G, DAS R. High-performance bidding agents for the continuous double auction; proceedings of the 3rd ACM conference on Electronic Commerce, NY, USA, F, 2001 [C]. ACM New York.
[81] TESAURO G, BREDIN J L. Strategic sequential bidding in auctions using dynamic programming; proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2, NY, USA, F, 2002 [C]. ACM New York.
[82] KEPHART J O, HANSON J E, GREENWALD A R. Dynamic pricing by software agents [J]. Computer Networks, 2000, 32(6): 731-52.
[83] LEVINSON D. The value of advanced traveler information systems for routechoice [J]. Transportation Research Part C, 2003, 11(1): 75-87.
[84] ROOZEMOND D A, ROGIER J L H. Agent controlled traffic lights; proceedings of the European Symposium on Intelligent Techniques, F, 2000 [C].
[85]全永燊,郭继孚,郑猛.我国城市道路车流离散规律初探[J].北京规划建设, 2001, 1: 33-4.
[86] TAN K K, KHALID M, YUSOF R. Intelligent traffic lights control by fuzzy logic [J]. Malaysian Journal of Computer Science, 1995, 9(2): 30-5.
[87] THORPE T L. Traffic light control using sarsa [D]. Colorado State University, 1997.
[88] TAALE H, BACK T, PREUSS M, et al. Optimizing traffic light controllers by means of evolutionary algorithms; proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing, F, 1998 [C].
[89] WAHLE J, SCHRECKENBERG M. A multi-agent system for on-line simulations based on real-world traffic data; proceedings of the 34th Annual Hawaii International Conference on System Sciences F,2001 [C].
[90] KESTING A, TREIBER M, LAMMER S, et al. Decentralized approaches to adaptive traffic control and an extended level of service concept; proceedings of the 17th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering, Bauhaus University Weimar, F, 2006 [C].
[91] BENJAMIN S C, JOHNSON N F, HUI P M. Cellular automata models of traffic flow along a highway containing a junction [J]. Journal of Physics A: Mathematical and General, 1996, 29(12): 3119-27.
[92] SIMON P M, NAGEL K. Simplified cellular automaton model for city traffic [J]. Physical Review E, 1998, 58(2): 1286-95.
[93] DIA H. An agent-based approach to modelling driver route choice behaviour under the influence of real-time information [J]. Transportation Research Part C, 2002, 10(5-6): 331-49.
[94] DAGANZO C F. In traffic flow, cellular automata= kinematic waves [J]. Transportation Research Part B, 2006, 40(5): 396-403.
[95] WIERING M, VAN VEENEN J, VREEKEN J, et al. Intelligent traffic light control [J]. European Research Consortium for Informatics and Mathematics, 2003, 53(40.
[96] SUTTON R S, BARTO A G. Reinforcement Learning: An Introduction [M]. MITPress, 1998.
[97] WIERING M. Multi-Agent Reinforcement Leraning for Traffic Light Control; proceedings of the Seventeenth International Conference on Machine Learning, F, 2000 [C]. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA.
[98] MAERIVOET S, DE MOOR B. Cellular Automata Models of Road Traffic [J]. Physics Reports, 2005, 419:1-64.
[99] BROCKFELD E, BARLOVIC R, SCHADSCHNEIDER A, et al. Optimizing traffic lights in a cellular automaton model for city traffic [J]. Physical Review E, 2001, 64(5): 56132.
[100]HUANG D, HUANG W. Traffic signal synchronization [J]. Physical Review E, 2003, 67(5): 56124.
[101]MAZUR F, CHROBOK R, HAFSTEIN S F, et al. Future of Traffic Information-Online-Simulation of a Large Scale Freeway Network; proceedings of the IADIS International Conference WWW/Internet 2004, F, 2004 [C].
[2] BONABEAU E, DORIGO M, THERAULAZ G. Swarm Intelligence: From Natural to Artificial Systems [M]. Oxford, 1999.
[3]金吾伦,郭元林.复杂性科学及其演变[J].复杂系统与复杂性科学, 2004, 1(1): 1-5.
[4] LANGTON C. Artificial Life, F, 1987 [C].
[5]贲可荣,张彦铎.人工智能[M].清华大学出版社.
[6] WOLFRAM S. Computation theory of cellular automata [J]. Communications in Mathematical Physics, 1984, 96(1): 15-57.
[7] CULIK K, HURD L P, YU S. Computation theoretic aspects of cellular automata [J]. Physica D 1990, 45(1):357-78.
[8] CONWAY J. The Game of Life [J]. Scientific American, 1970, 223(1): 120-23.
[9] EPSTEIN J M, AXTELL R. Growing Artificial Societies: Social Science from the Bottom Up [M]. Brookings Institution Press, 1996.
[10] DORIGO M, STüTZLE T. Ant Colony Optimization [M]. Mit Press, 2004.
[11] DORIGO M, GAMBARDELLA L M. Ant colony system: a cooperative learning approach to the travelingsalesman problem [J]. Evolutionary Computation, IEEE Transactions on, 1997, 1(1): 53-66.
[12] KENNEDY J, RC E. Particle swarm optimization; proceedings of the IEEE International Conference on Neural Networks, Piscataway, NJ, F, 1995 [C].
[13] EBERHART R, KENNEDY J. A new optimizer using particle swarm theory; proceedings of the Sixth International Symposium on Micro Machine and Human Science, F, 1995 [C].
[14] EBERHART R C, SHI Y. Comparison between Genetic Algorithms and Particle Swarm Optimization [J]. LECTURE NOTES IN COMPUTER SCIENCE, 1998, 611-8.
[15] EBERHART R, SHI Y. Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization; proceedings of the IEEE congress evolutionarycomputation,, San Diego, CA,, F, 2000 [C].
[16] CLERC M, KENNEDY J. The particle swarm-explosion, stability, and convergence in amultidimensional complex space [J]. IEEE Transactions on Evolutionary Computation,, 2002, 6(1): 58-73.
[17] KENNEDY J, EBERHART R. A discrete binary version of the particle swarm algorithm proceedings of the Proceedings of the conference on systems, man and cybernetics, , .Piscataway, New Jersey,, F, 1997 [C].
[18] AL-KAZEMI B, MOHAN C K. Multi-phase generalization of the particle swarm optimizationalgorithm, F, 2002 [C].
[19] AFSHINMANESH F, MARANDI A, RAHIMI-KIAN A. A Novel Binary Particle Swarm Optimization Method Using Artificial Immune System [M]. The International Conference on Computer as a Tool. 2005.
[20] CARLISLE A, DOZIER G. Adapting particle swarm optimization to dynamic environments [M]. International Conference on Artificial Intelligence. 2000: 429-34.
[21] KENNEDY J. The particle swarm: social adaptation of knowledge [M]. IEEE International Conference on Evolutionary Computation. 1997: 303-8.
[22] WEISS G. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence [M]. MIT Press, 1999.
[23]刘大有,杨鲲,陈建中. Agent研究现状与发展趋势[J].软件学报, 2000, 11(3): 315-21.
[24] HAN J, LIU J, CAI Q. From Alife Agents to a Kingdom of N Queens [J]. Intelligent Agent Technology: Systems, Methodologies, and Tools, 1999, 110-20.
[25] LIU J, HAN J, TANG Y Y. Multi-agent oriented constraint satisfaction [J]. Artificial Intelligence, 2002, 136(1): 101-44.
[26] AXELROD R. The Complexity of Cooperation: Agent-based Models of Competition and Collaboration [M]. Princeton University Press, 1997.
[27] SANDHOLM T W, CRITES R H. Multiagent reinforcement learning in the Iterated Prisoner's Dilemma [J]. BioSystems, 1996, 37(1-2): 147-66.
[28] AXELROD R, HAMILTON W D. The evolution of cooperation [J]. Science, 1981, 211(4489): 1390-6.
[29]米传民,刘思峰,江可申.经济学研究的新范式:刍议基于agent的计算经济学[J].山东经济2004, 6(28-33.
[30] TESFATSION L. Agent-based computational economics: modeling economies as complex adaptive systems [J]. Information Sciences, 2003, 149(4): 262-8.
[31]刘晓光,刘晓峰.计算经济学研究新进展——基于Agent的计算经济学透视[J].经济学动态, 2003, 11(58-61.
[32] BELL A M. Reinforcement Learning Rules in a Repeated Game [J]. Computational Economics, 2001, 18(1): 89-110.
[33] DAWID H. Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models [M]. Springer-Verlag New York, Inc. Secaucus, NJ, USA, 1999.
[34] EPSTEIN J M. Learning to Be Thoughtless: Social Norms and Individual Computation [J]. Computational Economics, 2001, 18(1): 9-24.
[35] WAN H A, HUNTER A, DUNNE P. Autonomous Agent Models of Stock Markets [J]. Artificial Intelligence Review, 2002, 17(2): 87-128.
[36] ARTHUR W B, HOLLAND J H, LEBARON B, et al. Asset Pricing Under Endogenous Expectations in an Artificial Stock Market [J]. The economy as an evolving complex system II, 1001(15-44.
[37] LEBARON B. Agent-based computational finance: Suggested readings and early research [J]. Journal of Economic Dynamics and Control, 2000, 24(5-7): 679-702.
[38] CARLEY K M. Computational organizational science and organizational engineering [J]. Simulation Modelling Practice and Theory, 2002, 10(5-7): 253-69.
[39] LUNA F, PERRONE A. Agent-Based Methods in Economics and Finance: Simulations in Swarm [M]. Kluwer Academic Publishers, 2001.
[40] COLELLA V S, KLOPFER E, RESNICK M. Adventures in Modeling: Exploring Complex, Dynamic Systems with StarLogo [J]. 2001,
[41] PREIST C, VAN TOL M. Adaptive agents in a persistent shout double auction [M]. ACM Press New York, NY, USA. 1998: 11-8.
[42] NAGEL K, SCHRECKENBERG M. A cellular automaton model for freeway traffic [J]. Journal de Physique I 1992, 2(1)2221-9.
[43] BIHAM O, MIDDLETON A A, LEVINE D. Self-organization and a dynamical transition in traffic-flow models [J]. Physical Review A, 1992, 46(10): 6124-7.
[44] WIERING M. Multi-Agent Reinforcement Leraning for Traffic Light Control [M]. Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA. 2000: 1151-8.
[45] WANG K P, HUANG L, ZHOU C G, et al. Particle swarm optimization for traveling salesman problem; proceedings of the Second International Conference on Machine Learning and Cybernetics, F, 2003 [C].
[46] JOHNSON D S, MCGEOCH L A. The travelling salesman problem: a case study in local optimization [M]. New York: Wiley and Sons, 1997.
[47] REINELT G. TSPLIB-A Traveling Salesman Problem Library [J]. ORSA Journal on Computing, 1991, 3:376-84.
[48]王康平,郭东伟,周春光.基于信念的多Agent模型解决多目标优化问题[J].计算机研究与发展, 2006, 43(z1): 157-61.
[49] WANG K, ZHOU C, GUO D, et al. Evolutionary Multi-Agent Model with Intent Exchange Solving Multi Objective Optimization Problem; proceedings of the Second International Conference on Complex Systems and Applications-Modeling, Control and Simulations, F, 2007 [C].
[50] COHON J L. Multiobjective Programming and Planning [M]. Dover Publications, 2004.
[51] STEUER R E. Multiple Criteria Optimization: Theory, Computation, and Application [M]. New York: Wiley, 1986.
[52] KOSKI J. Multicriterion Optimization in Structural Design [M]. Storming Media. 1981.
[53] ROSENBERG R S. Simulation of genetic population with bio-chemical properties [D]; University of Michigan, 1967.
[54] SCHAFFER J D. Multiple Objective Optimization with Vector Evaluated Genetic Algorithms [M]. Proceedings of the 1st International Conference on Genetic Algorithms. NJ, USA; Lawrence Erlbaum Associates, Inc. Mahwah. 1985: 93-100.
[55] FONSECA C M, FLEMING P J. Multiobjective optimization and multiple constraint handling withevolutionary algorithms. I. A unified formulation [J]. IEEE Transactions on Systems, Man and Cybernetics, Part A, 1998, 28(1): 26-37.
[56] HORN J, NAFPLIOTIS N, GOLDBERG D E. A niched Pareto genetic algorithm for multiobjective optimization; proceedings of the First IEEE Conference on Evolutionary Computation, Piscataway, F, 1994 [C]. IEEE Service Center.
[57] ZITZLER E, THIELE L. Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach [J]. Evolutionary Computation, IEEE Transactions on, 1999, 3(4): 257-71.
[58] ZITZLER E, LAUMANNS M, THIELE L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm, F, 2001 [C].
[59] SRINIVAS N, DEB K. Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms [J]. Evolutionary Computation, 1994, 2(3): 221-48.
[60] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. Evolutionary Computation, IEEE Transactions on, 2002, 6(2): 182-97.
[61] HU X, EBERHART R. Multiobjective optimization using dynamic neighborhood particleswarm optimization; proceedings of the Congress on Evolutionary Computation, F, 2002 [C].
[62] FIELDSEND J, SINGH S. A multi-objective algorithm based upon particle swarm optimisation; proceedings of the UK Workshop on Computational Intelligence, F, 2002 [C].
[63] COELLO C A C, LECHUGA M S. MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization; proceedings of the Congress on Evolutionary Computation F,2002 [C].
[64] PARSOPOULOS K E, VRAHATIS M N. Particle swarm optimization method in multiobjective problems; proceedings of the 2002 ACM symposium on Applied computing, NY, USA, F, 2002 [C]. ACM New York.
[65] MOSTAGHIM S, TEICH J. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO); proceedings of the 2003 IEEE Swarm Intelligence Symposium, F, 2003 [C].
[66] ABBASS H A. The Self-Adaptive Pareto Differential Evolution Algorithm; proceedings of the Congress on Evolutionary Computation F].
[67] MADAVAN N K, DIV N A S, CENTER N A R, et al. Multiobjective optimization using a Pareto differential evolutionapproach; proceedings of the 2002 Congress on Evolutionary Computation F,2002 [C].
[68] XUE F, SANDERSON A C, GRAVES R J. Pareto-based multi-objective differential evolution; proceedings of the 2003 Congress on Evolutionary Computation F,2003 [C].
[69] BABU B V, JEHAN M M L. Differential evolution for multi-objective optimization; proceedings of the 2003 Congress on Evolutionary Computation F,2003 [C].
[70] HAJELA P, LIN C Y. Genetic search strategies in multicriterion optimal design [J]. Structural and Multidisciplinary Optimization, 1992, 4(2): 99-107.
[71] VELDHUIZEN D A V, LAMONT G B. Multiobjective evolutionary algorithm test suites; proceedings of the 1999 ACM symposium on Applied computing, F, 1999 [C]. ACM Press New York, NY, USA.
[72]张利彪.基于粒子群和微分进化的优化算法研究[D].长春;吉林大学, 2007.
[73] FOSTER J. From simplistic to complex systems in economics [J]. Cambridge Journal of Economics, 2005, 29(6): 873-92.
[74] BONABEAU E. Agent-based modeling: Methods and techniques for simulating human systems [M]. National Acad Sciences. 2002: 7280-7.
[75] AXELROD R. Advancing the Art of Simulation in the Social Sciences [J]. Complexity, 1997, 3(2): 16-22.
[76] GODE D K, SUNDER S. Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality [J]. Journal of Political Economy, 1993, 101(1): 119.
[77] CLIFF D, BRUTEN J. Zero Not Enough: On The Lower Limit of Agent Intelligence For Continuous Double Auction Markets [J]. HP LABORATORIES TECHNICAL REPORT HPL, 1997,
[78] GJERSTAD S, DICKHAUT J. Price Formation in Double Auctions [J]. Games and Economic Behavior, 1998, 22(1): 1-29.
[79] PARK S, DURFEE E H, BIRMINGHAM W P. An adaptive agent bidding strategy based on stochastic modeling; proceedings of the third annual conference on Autonomous Agents, NY, USA, F, 1999 [C]. ACM New York.
[80] TESAURO G, DAS R. High-performance bidding agents for the continuous double auction; proceedings of the 3rd ACM conference on Electronic Commerce, NY, USA, F, 2001 [C]. ACM New York.
[81] TESAURO G, BREDIN J L. Strategic sequential bidding in auctions using dynamic programming; proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2, NY, USA, F, 2002 [C]. ACM New York.
[82] KEPHART J O, HANSON J E, GREENWALD A R. Dynamic pricing by software agents [J]. Computer Networks, 2000, 32(6): 731-52.
[83] LEVINSON D. The value of advanced traveler information systems for routechoice [J]. Transportation Research Part C, 2003, 11(1): 75-87.
[84] ROOZEMOND D A, ROGIER J L H. Agent controlled traffic lights; proceedings of the European Symposium on Intelligent Techniques, F, 2000 [C].
[85]全永燊,郭继孚,郑猛.我国城市道路车流离散规律初探[J].北京规划建设, 2001, 1: 33-4.
[86] TAN K K, KHALID M, YUSOF R. Intelligent traffic lights control by fuzzy logic [J]. Malaysian Journal of Computer Science, 1995, 9(2): 30-5.
[87] THORPE T L. Traffic light control using sarsa [D]. Colorado State University, 1997.
[88] TAALE H, BACK T, PREUSS M, et al. Optimizing traffic light controllers by means of evolutionary algorithms; proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing, F, 1998 [C].
[89] WAHLE J, SCHRECKENBERG M. A multi-agent system for on-line simulations based on real-world traffic data; proceedings of the 34th Annual Hawaii International Conference on System Sciences F,2001 [C].
[90] KESTING A, TREIBER M, LAMMER S, et al. Decentralized approaches to adaptive traffic control and an extended level of service concept; proceedings of the 17th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering, Bauhaus University Weimar, F, 2006 [C].
[91] BENJAMIN S C, JOHNSON N F, HUI P M. Cellular automata models of traffic flow along a highway containing a junction [J]. Journal of Physics A: Mathematical and General, 1996, 29(12): 3119-27.
[92] SIMON P M, NAGEL K. Simplified cellular automaton model for city traffic [J]. Physical Review E, 1998, 58(2): 1286-95.
[93] DIA H. An agent-based approach to modelling driver route choice behaviour under the influence of real-time information [J]. Transportation Research Part C, 2002, 10(5-6): 331-49.
[94] DAGANZO C F. In traffic flow, cellular automata= kinematic waves [J]. Transportation Research Part B, 2006, 40(5): 396-403.
[95] WIERING M, VAN VEENEN J, VREEKEN J, et al. Intelligent traffic light control [J]. European Research Consortium for Informatics and Mathematics, 2003, 53(40.
[96] SUTTON R S, BARTO A G. Reinforcement Learning: An Introduction [M]. MITPress, 1998.
[97] WIERING M. Multi-Agent Reinforcement Leraning for Traffic Light Control; proceedings of the Seventeenth International Conference on Machine Learning, F, 2000 [C]. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA.
[98] MAERIVOET S, DE MOOR B. Cellular Automata Models of Road Traffic [J]. Physics Reports, 2005, 419:1-64.
[99] BROCKFELD E, BARLOVIC R, SCHADSCHNEIDER A, et al. Optimizing traffic lights in a cellular automaton model for city traffic [J]. Physical Review E, 2001, 64(5): 56132.
[100]HUANG D, HUANG W. Traffic signal synchronization [J]. Physical Review E, 2003, 67(5): 56124.
[101]MAZUR F, CHROBOK R, HAFSTEIN S F, et al. Future of Traffic Information-Online-Simulation of a Large Scale Freeway Network; proceedings of the IADIS International Conference WWW/Internet 2004, F, 2004 [C].