摘要
多UCAV(Unmanned Combat Aerial Vehicle)协同作战将成为未来战争的一种重要方式。研究多UCAV协同任务规划建模与优化方法,为UCAV制定出协同的任务计划和飞行航迹,使多UCAV协同系统整体作战效能优于各UCAV独立作战效能的总和,是发挥多UCAV协同作战优势的关键,具有重要理论价值与现实意义。解决该问题的核心就是对多UCAV协同任务规划问题进行合理的建模和求解,以及适应环境动态变化的在线规划。论文借鉴模型预测控制(Model Predictive Control,MPC)思想,从问题建模、求解算法和滚动优化方法三个方面开展研究,主要工作及创新点如下:
(1)建立了多UCAV动态协同任务规划的基本数学模型。在深入分析多UCAV协同执行作战任务的独特性基础上,提出基于MPC的多UCAV协同任务规划建模方法。以包含敌方目标、威胁以及我方UCAV在内的复杂大系统作为被控对象,以UCAV任务计划与航迹计划作为控制输入,通过提取系统关键状态及其变化规律,建立了描述系统动态变化过程的预测模型和反映控制输入性能的优化模型。为了便于求解,根据不同时间粒度对基本数学模型进行递阶分解,将多UCAV协同任务规划问题分解为任务调度和航迹规划两个子问题,分别求解UCAV任务计划和航迹计划。进一步分析了这两个问题的组合复杂性,提出了分层求解方案。仿真实验表明,能否描述多UCAV协同执行任务过程的动态性是影响模型可用性的重要因素;本文建立的多UCAV动态协同任务规划模型能够描述这一特性,有利于提高控制效果。
(2)分析了粒子群优化算法(Particle Swarm Optimization,PSO)求解离散组合优化问题的两种设计原则,针对任务调度和航迹规划问题的排序和无序组合特性,提出了求解排序组合优化问题的连续空间离散PSO(Continuous Space DiscretePSO,CDPSO)算法和无序组合优化问题的离散空间离散PSO(Discrete Space DiscretePSO,DDPSO)算法。在CDPSO算法中,使用实数粒子编码方式,充分利用粒子位置的整数和小数部分,将离散问题映射到相对较小的连续粒子位置空间。提出了动态子群和双精度高斯位置扰动策略,使得粒子群能够较均匀地在问题空间搜索,避免陷入局部极值,在保持传统PSO算法快速收敛的同时,加强了算法局部搜索能力。DDPSO算法的核心是将PSO算法映射到离散问题空间,在经典PSO算法的基本框架下,通过重新定义粒子位置和速度在离散空间内的表示形式、加减法和数乘计算等运算法则,形成了一种全新的PSO算法模式。针对两类典型组合优化问题的实验结果表明,两种算法能够快速、稳定收敛到最优或次优解,搜索效率高。
(3)提出了基于任务滚动窗口与SA-CDPSO(Simulated Annealing CDPSO)算法的任务调度方法。针对在线任务调度问题的实时性要求,扩展MPC传统滚动优化方法,提出基于任务轴的滚动窗口优化方法,通过在线进行的优化计算与滚动实施,快速响应环境变化。研究了适合于任务调度的滚动窗口更新、滚动机制、局部优化问题构造方法。针对任务调度问题约束条件多、搜索空间不连续的特点,提出SA-CDPSO混合算法。使用CDPSO算法进行全局搜索,再根据CDPSO产生的初始解构造邻域,利用CDPSO的快速收敛特性和SA的局部搜索能力,确保算法能够快速收敛到最优解。基于正交实验法构造了具有代表性的测试问题实例,对SA-CDPSO算法和滚动窗口方法进行全面实验验证和分析。对于不同测试问题实例,SA-CDPSO算法都表现出稳定、优良的搜索能力;任务滚动窗口优化方法能适应于各种环境变化。
(4)提出了基于双精度滚动窗口与AI-DDPSO(Artifical Immune DDPSO)算法的航迹规划方法。针对在线航迹规划的特殊性,提出异步双精度滚动窗口策略,分别在两个信息粒度不同的窗口内进行局部精细航迹规划和全局粗略航迹规划,并按照不同方式采用不同频率推进窗口。重点研究了求解精细航迹的AI-DDPSO混合算法,结合UCAV航迹特有的最小直飞距离、最大转弯角、爬升/俯冲角、以及最小调整角度等约束,设计了基于角度偏差的粒子编码方式,确保航迹满足UCAV飞行约束。在DDPSO算法基础上,基于人工免疫(Artifical Immune,AI)原理,设计了粒子逃逸算子和群体排斥算子,通过检测粒子个体和群体浓度改变粒子运动轨迹,以保持个体和群体的多样性。实验表明,该方法可充分利用当前新信息,快速规划出较优的飞行航迹。
Using multiple UCAVs(Unmanned Combat Aerial Vehicle) fight cooperatively will be an important manner to perform a military mission in the future. Studying on the multi-UCAV cooperative mission planning modeling and optimizing methods is the key to take full advantage of multi-UCAV combating cooperatively, which is of significant theoretical value and great practical value. The problem focus on planning elegant task plan and path plan for each UCAV to make the integrated effects of multi-UCAV system combating cooperatively exceed the total effects of each UCAV combating separately. Properly modeling and solving, online planning for dynamic environment are the keys to solve this problem. Based on the model predict control(MPC) theory, this dissertation studies the problem model, algorithms and online optimizing methods for multi-UCAV cooperative mission planning. The main work and contributions are as follows:
(1) A basic mathematic model for multi-UCAV dynamic cooperative mission planning is presented. Following thorough analysis on the characteristics of multi-UCAV performing combat missions, a modeling method based on MPC is proposed. Setting the complex system includes targets, threats and UCAVs as the controlled object, setting the task plan and path plan of UCAVs' as the controlling inputs, a state-predict model reflects system's dynamics and an optimization model reflects the control inputs performance are presented, by extracting the primary states and their transformation of the whole system. By decomposing the basic mathematic model according to different time granularity, the multi-UCAV cooperative mission planning problem is devided into two subproblems for solving the task plan and path plan respectively, which are called task scheduling and path planning. Then with the analysis on the combination of each subproblem, a logic process for solving them is presented. Simulations show that characterizing the dynamics in the process of multi-UCAV completing mission is an important performance factor of planning model. And the model of multi-UCAV dynamic cooperative mission planning problem presented in this paper is beneficial to improve the effectiveness of control.
(2) Two different approaches for particle swarm optimization(PSO) algorithm solving discrete combinatorial optimizing problems(DCOP) are proposed. On analyzing the different combinatorial characters of task scheduling and path planning problems, two discrete PSO algorithms are presented. One is continuous space discrete PSO(CDPSO) for DCOP with scheduling, the other is discrete space discrete PSO(DDPSO) for DCOP without scheduling. In CDPSO algorithm, both the integer and decimal fraction of a particle's position are utilized simultaneously through real-number coding, so the discrete problem is mapped into a smaller continuous particle position space. Dynamic sub-swarms strategy and double precision gauss disturbances strategy make particles searching the problem space comprehensively and breaking away from the local extreme. Thus the algorithm performance in local searching is improved while keeping the convergence ability of traditional PSO. The key of the DDPSO is mapping the PSO algorithm into discrete problem space based on the framework of classical PSO algorithm. A totally new algorithm pattern is proposed by redefining the particles' position, velocity and their operation rules. The tests results for two typical DCOP show that those algorithms can both find the best or hypo-best result quickly and steadily with high efficiency.
(3) An online task scheduling method based on rolling window and SA-CDPSO (Simulated Annealing CDPSO) algorithm is presented. Since task scheduling online needs to respond realtime, a rolling window optimizing approach is proposed by expanding traditional rolling horizon approach of MPC. The approach can respond very quickly for environment changes by optimizing and rolling online. Rolling rules, rolling window updating and local optimizing problem formatting approaches fit for task scheduling problem are studied. A SA-CDPSO hybrid algorithm is proposed for the task scheduling problem for its discontinuous problem space caused by various constraints of the problem. A result in the global problem space is found using CDPSO algorithm, and then the neighborhood around the result is constructed and searching in the neighborhood is carried out using SA algorithm. This hybrid technique ensures algorithm converging at the best result quickly. A set of test instances which reflects different mathematical properties of the problem are constructed via orthogonal design method for verifying the performance of the SA-CDPSO algorithm and rolling window method. Experiments show that the SA- CDPSO algorithm can solve all of the instances effectively with steady and excellent performance, which proves the rolling window method is adaptive to varieties in environment.
(4) An online path planning method based on double precision rolling windows and AI-DDPSO (Artifical Immune DDPSO) algorithm is presented. A double precision rolling windows strategy for online path planning is proposed. Local elaborate path and global cursory path are planned respectively in different window with different information granularity. Two windows roll on different frequency with different mode. An AI-DDPSO hybrid algorithm is proposed for elaborate path planning. An angle err based particle coding method is presented, taking into account the restrictions in UCAV path planning, including minimal straight flying distance, maximal turning angle, climbing-diving angle and minimal adjusting angle. Escape operator and repulsion operator based on artifical immune(AI) theory are proposed, which can keep the diversity of particle swarm by checking the density of particle individuals and swarm to change particles tracks. This method can utilize current information sufficiently and get an optimum path for UCAV quickly.
引文
[1]Fahlstrom Paul G.,Gleason Thomas J.著,吴汉平等译.无人机系统导论.电子工业出版社,2003
[2]陈哨东,孙隆和.先进无人战斗机(UCAV)系统概念.火力与指挥控制.March 2003,28(6):10-13
[3]程龙,陈昌金,张健康,黄俊.一支新兴的空中力量--无人作战飞机.飞航导弹.2005,(5):33—37
[4]Flach J.M.,Eggleston R.G.,Kuperman G.G.无人作战飞机的未来作战用途.国际航空.2003,(7)
[5]Office of the Secretary of Defense.Unmanned Aerial Vehicle(UAV) Roadmap 2005-2030.August 2005
[6]Durham James M.Raleigh.Emerging DoD UCAV Vision,OSD(PA&E).March 2003
[7]Eun Y.,Bang H.Cooperative Control of Multiple UCAVs for Suppression of Enemy Air Defense.AIAA 3rd "Unmanned Unlimited" Technical Conference.Chicago,Illinois,2004
[8]Hathaway David C.Germinating a New SEAD:The Implications of Executing the SEAD Mission in a UCAV.June 2001
[9]Flach J.M.,Eggleston R.G.,Kuperman G.G.SEAD and the UCAV A Preliminary Cognitive Systems Analysis.AERL-HE-WP-TR-1998-0013.February 1998
[10]Freiberg Bill.DARPA/USAF Unmanned Combat Air Vehicle System Demonstration Program.May 2002
[11]余旭东.未来作战中无人机作战使用十大方式.飞航导弹.2005,(4):30-32
[12]世界无人机大全.航空出版社版,2004
[13]徐文.德国的无人机.飞航导弹.2005,(6):14-18
[14]李果.德国开始研制无人作战飞机验证机.国际航空杂志.2005,(5):35
[15]新浪军事网.英国国防部推进无人作战飞机(UCAV)研究工作(转摘自《航空航天技术周刊》).[Online].http://mil.news.sina.com.cn/2005-11-14/0814330721.htm
[16]法国重视无人战斗飞行器的开发.动态与信息.2004,35(2):51
[17]郭峰.”天空新宠”-浅析无人机未来作战运用趋势.国防科技.2005,7:61—64
[18]Banda Siva,Doyle John,Murray Richard.Research Needs in Dynamics and Control for Uninhabited Aerial Vehicles(UAVs),1997
[19]Murphey R.,Pardalos P.M.Cooperative Control and Optimization(Applied Optimization ).Springer,2002
[20]陈洪主编.美国空军战役作战研究.空军指挥学院出版,2000
[21]于长江.航空兵战术.空军指挥学院出版
[22]人民网.作战技术超前数十年 美空军六大基础研究课题揭密.[Online].http://www.people.com.cn/BIG5/junshi/62/20020624/759999.html
[23]Johnson Christopher L.Inverting the Control Ratio:Human Control of Large Autonomous Teams.Proceedings of the the International Conference on Autonomous Agents and Multi-Agent Systems.Melbourne,Australia,2003
[24]Heise S.Mixed Initiative Control of Automa-teams(MICA).[Online].http://www.cds.caltech.edu/murry/projects/darpa01-mica/
[25]Michael Ownby.Mixed Initiative Control of Automa-teams(MICA) - A Progress Report.AIAA 3rd 'Unmanned Unlimited' Technical Conference.Chicago,Illinois,2004
[26]Autonomous Negotiating Teams Program(ANT).[Online].http://www.if.afrl.af.mil /div/iftb/ants.html/
[27]新浪军事网.约翰斯·霍普金斯大学为美军J-UCAS开发COS.[Online].http://jczs.news.sina.com.cn
[28]COMETS project official web page.[Online].http://www.comets-uavs.org/
[29]Campbell M.,Andrea R.D.,Schneider D.,Chaudhry A.RoboFlag Games using Systems Based,Hierarchical Control.American Control Conference.2003,661-666
[30]Secrest Bary R.Traveling Salesman Problem for Surveillance Mission Using Particel Swarm Optimization.Master's thesis,Air Force Institute of Technology,Wright-Patterson Air Force Base,Ohio,USA,March 2001
[31]Brown D.T.Routing Unmmanned Aerial Vehicles while Considering General Restricted Operating Zones.Master's thesis,Air Force Institute of Technology,Wright-Patterson Air Force Base,Ohio,USA,March 2001
[32]Ryan A.,Zennaro M.,Howell A.,Sengupta R.,Hedrick J.K.An Overview of Emerging Results in Cooperative UAV Control.43rd IEEE Conference on Decision and Control.2004,vol.1
[33]Yavuz Kursat.Multiobjective Mission Route Planning using Particle Swarm Optimization.Master's thesis,Air Force Institute of Technology Air University,2002
[34]Hutchison M.G.A Method for Estimating Range Requirements of Tactical Reconnaissance UAVs.AIAA's 1st Technical Conference and Workshop on Unmanned Aerospace Vehicles. Virginia, 2002
[35] Lua C.A., Altenburg K., Nygard K.E. Synchronized Multi-Point Attack by Autonomous Reactive Vehicles with Simple Local Communication. IEEE Swarm Intelligence Symposium. Indianapolis, Indiana, 2003
[36] Schumacher C, Chandler P. R., Rasmussen S. R. Task Allocation for Wide Area Search Munitions via Network flow Optimization. AIAA Guidance, Navigation, and Control Conference and Exhibit. Montreal, Canada, 2001
[37] Schumacher C, Chandler P. R., Rasmussen S. R. Task Allocation for Wide Area Search Munitions via Iterative Network flow. AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, California, 2002
[38] Nygard K. E., Chandler P. R., Pachter M. Dynamic Network Flow Optimization Models for Air Vehicle Resource Allocation. American Control Conference. Arlington, VA, 2001, 1853-1858
[39] Schumacher C, Chandler P. R., Pachter M., Pachter L. S. UAV Task Assignment with Timing Constraints. AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas, 2003
[40] Schumacher C, Chandler P. R., Pachter M., Pachter L. S. UAV Task Assignment with Timing Constraints via Mixed-Integer Linear Programming. AIAA 3rd
[41] Schumacher C, Chandler P. R., Pachter M., Pachter L. S. Constrained Optimization for UAV Task Assignment. AIAA Guidance, Navigation, and Control Conference and Exhibit. Providence, Rhode Island, 2004
[42] Darrah M. A., Niland W. M., Stolarik B. M. Multiple UAV Dynamic Task Allocation using Mixed Integer Linear Programming in a SEAD Mission. AIAA Infotech@Aerospace Conference, AIAA 2005-7164,. Alexandria, VA, 2005
[43] Shima T., Rasmussen S. J., Sparks A. G., Passino K. M. Multiple Task Assignmentds for Cooperating Uninhabited Aerial Vehicls Using Genetic Algorithms. Computer and Operations Research. 2005
[44] Jin Yan, Liao Yan, Minai Ali A., Polycarpou Marios M. Cooperative Real-Time Search and Task Allocation in UAV Teams
[45] Jin Yan, Liao Yan, Minai Ali A., Polycarpou Marios M. Balancing Search and Target Response in Cooperative Unmanned Aerial Vehicle(UAV) Teams. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cyernetics. 2006, 36(3):571-587
[46] Atkinson M. L. Contract Nets for Control of Distributed Agents in Unmanned Air Vehicles. 2nd AIAA Unmanned Unlimited Systems,Technologies, and Operations- Aerospace. san Diego, California, 2003
[47] Frazzoli Emilio. Robust Hybrid Control for Autonomous Vehicle Motion Planning. Ph.D. thesis, Massachusetts Institute of Technology, USA, 2001
[48] Latombe J. C. Robot Motion Planning. Kluwer Boston, 1991
[49] Hwing Y., Ahujia N. Cross Motion Planning - A Survey. ACM Computing Survey.1992, 24(3):219-2912
[50] Bellingham J., Tillerson M., Richards A., How J. P. Multi-Task Allocation and Path Planning for Cooperative UAVs. Cooperative Control: Models, Applications and Algorithms. Denver,CO, 2003, 23-41
[51] Kuwata Y., How J. P. Three Dimensional Receding Horizon Control for UAVs.AIAA Guidance, Navigation and Control Conference and Exhibit. 2004
[52] Beard R. W., McLain T. W., Goodrish M., Anderson E. P. Coordinated Target Assignment and Intercept for Unmanned Air Vehicles. IEEE Transactions on Robotics and Austomation. December 2002, 18(3):911-922
[53] McLain T. W., Chandler P. R., Pachter M. A Decomposition Strategy for Optimal Coordination of Unmanned Air Vehnicles. Proceedingings of the American Control Conference. Chicago, 2000, 369-373
[54] McLain T. W., Chandler P. R., et al. Cooperative Control of UAV Rendezvous.Proceedings of American Control Conference. 2001, 2309-2314
[55] Pettersson P.O., Doherty P. Probabilistic Roadmap Based Path Planning for an Autonomous Unmanned Aerial Vehicle. The Workshop on Connecting Planning and Theory with Practice. 2004
[56] Kavraki L., Sevestka P., Latombe J-C, et al. Probalistic Roadmaps for Path Planning in High Dimensional Configuration Spaces. IEEE Transactions on Robotics and Automation. 1996, 12(4):566-580
[57] Chandler P. R., Rasmussen S., Pachter M. UAV Cooperative Path Planning. AIAA Guidance, Navigation, and Control Conference and Exhibit. Denver,Colorado, 2000
[58] McLain T. W., Beard R. W. Trajectory Planning For Coordinated Rendevous of Unmanned Air Vehicles. AIAA Guidance, Naigation, and Control Conference and Exhibit. Denver, Colorado, 2000
[59] Alighanbari M., Kuwata Y., How J. P. Coordination and Control of Multiple UAVs with Timing Constraints and Loiterings. American Control Conference. Denver,Colorado, 2003, 5311-5316
[60] McLain T. W., Beard R. W. Cooperative Path Planning for Timing-Critical Mis- sions. Proceedings of the American Control Conference. Denver, Colorado, 2003,296-301
[61] Szczerba R. J., Galkowski P., et al. Robust Algorithm for Real-Time Route Planning
[62] Capozzi B. J. Evolution-based Path Planning and Management for Automonous Vehicles. Ph.D. thesis, University of Washington, 2001
[63] Y. Gotoand, A. Stentz. Mobile Robot navigation: The CMU System. IEEE Expert.1987, 2(4):44-55
[64] Korf R. Real-Time Heuristic Search, Artificial Intelligence. 1990, 42(2): 189-211
[65] Sjanic Zoran. On-line Mission Planning based on Model Predictive Control. Master's thesis, Division of Automatic Control Department of Electrical Engineering Linkoing University, 2001
[66] Bellingham J., Richards A., How J. P. Receding Horizon Control of Autonimous Aireal Vehicle
[67] Bellingham J., Tillerson M., Alighanbari M., How J. P. Cooperative Path Planning for Multiple UAVs in Dynamic and Uncertain Environments
[68] Richards A., How J. P. Decentralized Model Predictive Control of Cooperating UAVs
[69] Ryan J. L., Bailey G. T., Moore J. T., Carlton W. B. Reactive Tabu Search in Unmanned Aerial Reconnaissance Simulations. Winter Simulation Conference. 1998,873-879
[70] O'Rourke K. P., Baileyand T. G., Hill R., Carlton W. B. Dynamic Routing of Unmanned Aerial Vehicles Using Reactive Tabu Search. Military Operations Research Journal. 2001, 6:5-30
[71] Rasmussen S., Chandler P. R. Optimal vs. Heuristic Assignment of Cooperative Autonomous Unmanned Air Vehicles. AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas, 2003
[72] Chen Genshe, Cruz Jose B. Jr. Genetic Algorithm for Task Allocation in UAV Cooperative Control. AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas, 2003
[73] Cruz Jose B. Jr, Chen Genshe andLi Dongxu, Wang Xu. Particle Swarm Optimization for Resource Allocation in UAV Cooperative Control. AIAA Guidance,Navigation, and Control Conference and Exhibit. Chicago, Illinois, 2004
[74] Pongpunwattana Anawat. Real-Time Planning for teams of Autonomous Vehicles in Dynamic Uncertain Environments. Ph.D. thesis, University of Washington, Wash- ington,USA,2004
[75]Rasmussen D.,Kragelund S.,Pongunwattana A.,Capozzi B.An Evolution based Path Planning Algorithm for Autonomous Motion of a UAV Through Uncertain Environments.AIAA 21st Digital Avionics System Conference.Irvine,CA,2003
[76]Nikolos I.K.Evolutionary Algorithm based Offline/Online Path Planner for UAV Navigation.IEEE Transactions on System,Man,and Cybernetics-Part B:Cybernetics.2003,33(6):898-912
[77]符小卫,高晓光.一种无人机路径规划算法研究.系统仿真学报.2004,14(1):20-21+34
[78]肖秦琨,高晓光.基于空间改进型Voronoi图的无人机路径规划研究.计算机工程与应用.2005:204—207
[79]叶媛媛.多UCAV协同任务规划方法研究.国防科学技术大学博士学位论文,长沙,中国,2005
[80]郑昌文,丁明跃,周成平,苏康,袁鸿翼,金惠香.多飞行器协调航迹规划方法.宇航学报.2003,24(2):115-120
[81]柳长安,王和平,李为吉.攻击无人机的协同航路规划.西北工业大学学报.2003,21(6):707—710
[82]高晓光,符小卫,宋绍梅.多UCAV航迹规划研究.系统工程理论与实践.2004,24(5):140—143
[83]严平,丁明跃,周成平,郑昌文.飞行器多任务在线实时航迹规划.航空学报.2004,25(9):485—489
[84]唐强,张新国,刘锡成.一种用于低空飞行的在线航迹重规划方法.西北工业大学学报.2005,23(2):271—275
[85]余舟毅,陈宗基,周锐.基于遗传算法的动态资源调度问题研究.控制与决策.2004,19(11):1308-1311
[86]冯琦,周德云.应用单亲遗传算法进行大规模UCAVs任务分配.火力与指挥控制.2006,31(5):18-21
[87]龙涛.多UCAV协同任务控制中分布式任务分配与任务协调技术研究.国防科学技术大学博上学位论文,长沙,中国,2006
[88]田菁.多无人机协同侦察任务问题建模与优化技术研究.国防科学技术大学博士学位论文,长沙,中国,2007
[89]曹菊红,高晓光.多架无人机协同作战智能指挥控制系统.火力与指挥控制.2003,28(5):22—24
[90]刘金星,佟明安.多架无人作战飞机攻击行为的协调.飞行力学.2003,21(2):16 19
[91]Breger L.S.Model Predictive Control for Formation Flying Spacecraft.Master's thesis,Massachusetts Institute of Technology,2002
[92]Chandler P.R.,Pachter M.,Rasmussen S.UAV Cooperative Control.American Control Conference.2001,50-55
[93]席裕庚.动态不确定环境下广义控制问题的预测控制.控制理论与应用.2000,17(5):665—670
[94]张纯刚.机器人滚动路径规划的理论与方法研究.上海交通大学博士学位论文,上海,中国,2002
[95]赵鸿昌,刘志远,刘志林.MPC在飞行器底层控制及高层决策中的应用.控制工程.2004,11(4):356—359
[96]冯少辉.模型预测控制工程软件关键技术及应用研究.浙江大学博士学位论文,杭州,中国,2003
[97]徐祖华.模型预测控制理论及应用研究.浙江大学博士学位论文,杭州,中国,2004
[98]Slater G.L.Cooperative Between UAVs in a Search and Destroy Mission.AIAA Guidance,Navigation,and Contrl Conference and Exhibit.Astin,Texas,2003
[99]Chandler P.R.,Pachter M.Research Issues in Autonomous Control of Tactical UAVs.American Control Conference.Philadephia,Pennsylvania,1998
[100]Kuwata Yoshiaki.Real-time Trajectory Design for Unmanned Aerial Vehicles using Receding Horizon Control.Master's thesis,Massachusetts Institute of Technology,Massachusetts,USA,June 2003
[101]Alighanbari Mehdi.Task Assignment Algorithm for Teams of UAVs in Dynamic Enviroments.Master's thesis,Massachusetts Institute of Technoligy,Massachusetts,USA,June 2004
[102]King Ellis T.Distributed Coordination and Control Experiments on a Multi-UAV Testbed.Master's thesis,Massachusetts Institute of Technoligy,Massachusetts,USA,Sepetmber 2004
[103]Li Yangmin,Chen Xin.Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN.ICNC2005,LNCS 3612.2005,628-631
[104]Kenny J.,Spears W.M.Matching Algorithm to Problems:an Experimental Test of the Particle Swarm and Some Genetic Algorithm on the Multimodal Problem Generator.International Conference on Evolutionary Computation.Piscataway,NJ,1999,78-83
[105]Carlisle A.,Dozier G.Adapting Particle Swarm Optimization to Dynamic Environments.Intelnational Confonference on Artificial Intelligence.Las Vegas,Nevada,USA,2000,429-434
[106]Kennedy J.,Eberhart R.C.Particle Swarm Optimization.IEEE International Conference on Neural Networks.Perth,Australia,1995,1942-1948
[107]Eberhart R.C.,Kennedy J.Swarm Intelligence.Morgan Kaufmann
[108]Shi Y.H.,Eberhart R.C.A Modified Particle Swarm Optimizer.IEEE International Conference on Evolutionary Computation,Piscataway.1998,69-73
[109]Shi Y.H.,Eberhart R.C.Parameter Selection in Particle Swarm Optimization.Proceedings of the 7th Annual Conference on Evolutionary Programming.Washington DC,1998,591-600
[110]Shi Y.H.,Eberhart R.C.Empirical Study of Particle Swarm Optimizationn.Proceedings of Congresson Evolutionary Computation.Washington DC,1999,1945-1950
[111]Suganthan P.N.Particle Swarm Optimiser with Neighborhood Operator.Congress on Evolutionary Computation.Washington DC,1999,1958-1962
[112]Shi Y.H.,Eberhart R.C.Fuzzy Adaptive Particle Swarm Optimization.IEEE International Conference on Evolutionary Computation.Seoul,Korea,2001,101-106
[113]Kennedy J.The Particle Swarm:Social Adaptation of Knowledge.IEEE International Conference on Evolutionary Computation.Indianapolis,Indiana,1997,303-308
[114]Ender P.,Mohan C.K.Particle Swarm Optimization:Surfing the Waves.Congress of Evolutionary Computation.Washington DC,1999,1939-1944
[115]Eberhart R.C.andShi Y.H.Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization.IEEE Conference on Evolutionary Computation.San Diego,2000,84-88
[116]El-Gallad A.,El-Hawary M.,Sallam A.,Kalas A.Enhancing the Particle Swarm Optimizer via Proper Parameters Selection.IEEE CCECE02 Proceedings.Piscataway,N J,Canadian,2002,792-797
[117]Trelea I.C.The Particle Swarm Optimization Algorithm:Convergence Analysis and Parameter Selection.Information Processing Letters.Piscataway,NJ,2003,317-325
[118]彭宇,彭喜元,刘兆庆.微粒群算法参数效能的统计分析.电子学报.2004,32(2):209—213
[119]Kennedy J.Small Worlds and Mega-minds:Effects of Neighborhood Topology on Particle Swarm Performance.IEEE Congress on Evolutionary Computation.Piscataway,N J,1999,1931-1938
[120]Angeline P.J.Evolutionary Optimization versus Particle Swarm Optimization:Philosophy and Performance Difference.Proceedings of the 7th Annual Conf on Evolutionary Programming.Gemany,1998,601-610
[121]李宁,邹彤,孙德宝.车辆路径问题的粒子群算法研究.系统工程学报.2004,19(6):596-600
[122]Lovbjerg M.andRasmussen T.K.,Krink T.Hybrid Particle Swarm Optimizer with Breeding and Subpopulations.Peoceedings of Genetic and Evolutionary Computation Conference.San Francisco,California,2001,469-476
[123]Angeline P.J.Using Selection to Improve Particle Swarm Optimization.IEEE International Conference on Evolutionary Computation.Anchorage,Alaska,1998
[124]Higashi N.,Iba H.Particle Swarm Optimization with Gaussian Mutation.Congress on Evolutionary Computation.Piscataway,NJ,2003,72-79
[125]吕振肃,侯志荣.自适应变异的粒子群优化算法.电子学报.2004,32(3):416—420
[126]高鹰,谢胜利.基于模拟退火的粒子群优化算法.计算机工程与应用.2004,20(5):47—50
[127]Afshinmanesh F.,Marandi A.,Rahimi-Kian A.A Novel Binary Particle Swarm Optimization Method Using Artificial Immune System.EUROCON.Serbia & Montenegro,Belgrade,2005
[128]Bergh F.,Andries P.,Engelbrech.A Cooperative Approach to Particle Swarm Optimization.IEEE Transactions on Evolutionary Computation.2004,8(3):225-239
[129]李爱国.多粒子群协同优化算法.复旦学报(自然科学版).2004:923-925
[130]孟红记,郑鹏,梅国晖,谢植.基于混沌序列的粒子群优化算法.控制与决策.2006,21(3):263-266
[131]Eberhart R.C.,Hu X.H.Human Tremor Analysis using Particle Swarm Optimization.Congress on Evolutionary Computation.1999,1927-1930
[132]Abido M.A.Optimal Design of Power-System Stabilizer using Particle Swarm Optimization.IEEE Transactions on Energy Conversion.2002,17(3):406-413
[133]龙云,王健全.基于粒子群游算法的同步发电机参数辩识.大电机技术.2003,(1):8—11
[134]Gaing Z.L.A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System.IEEE TEANSACTIONS ON POWER SYSTEMS.2004,19(2):384-391
[135]Yoshida H.,Kawata K.,Fukuyama Y.,et al.A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Stability.Proceedings of IEEE International Conference on Intelligent System Application to Power Systems.Riode Janeiro,1999,117-121
[136]Huang C.M.,Huang C.J.,Wang M.L.A Particle Swarm Optimization to Identifying the ARMAX Model for Short-term Load Forecasting.IEEE TEANSACTIONS ON POWER SYSTEMS.2005,20(2):1126-1133
[137]余欣梅,李妍,熊信良.基于PSO考虑谐波影响的补偿电容优化配置.中国电机工程学报.2003,23(2):26-31
[138]Ting T.O.,Rao M.V.C.,Loo C.K.A Novel Approach for Unit Commitment Problem via an Effective Hybrid Particle Swarm Optimization.IEEE TEANSACTIONS ON POWER SYSTEMS.2006,21(1):411-418
[139]Fourie P.C.,Groenwold A.The Particle Swarm Optimization Algorithm in Size and Shape Optimization.Structure Multidisciplinary Optimization.2002,23(4):259-267
[140]Xie X.F.,Zhang W.J.,Yang Z.L.Incorporating Knowledge in Genetic Algorithms for Device Synthesis.6th International Conference on Solid-stateand Integrated Circuit Technology.Shanghai,2001,1143-1146
[141]侯志荣,振肃.IIR数字滤披器设计的粒子群优化算法.电路与系统学报.2003,8(4):16-20
[142]潘金科,王文宏,朱剑英.基于粒子群优化和模拟退火的混合调度算法.中国机械工程.2002,17(10):1044-1046+1064
[143]Eberhart R.,Shi Yuhui.Tracking and Optimizing Dynamic Systems with Particle Swarms.IEEE International Conference on Evolutionary Computation.Hawaii,2001,94-100
[144]Hu X.,Eberhart R.Multiobjective Optimization using Dynamic Neighborhood Particle Swarm Optimization.IEEE World Congress on Computational Intelligence.Piscataway,NJ,2002,1677-1681
[145]Parsopoulos K.,Vrahatis M.Particle Swarm Optimization in Noisy and Continuously Changing Environments.Artificial Intelligence and Soft Computing.2001:289294
[146]Cagnina L.,Esquivel S.A Particle Swarm Optimizer for Multi-Objective Optimization. JCS&T4.2005:204-210
[147]Coello C.A.C.,Lechuga M.S.MOPSO:A Proposal for Multiple Objective Particle Swarm Optimization.Congress on Evolutionary Computation.Piscataway,NJ,2002,1051-1056
[148]Mostaghim S.,Teich J.Strafegies for Finding Good Local Guides in Multi-objective Particle Swarm Optimization(MOPSO).IEEE Swarm Intelligence Symposium.Indiana,USA,2003,26-33
[149]Bartz-Bartz T.,Limbourg P.,Parsopoulos K.E.and Vrahatis M.N.Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques.Congress on Evolutionary Computation.Canberra,Australia,2003,vol.3,1780-1787
[150]Parsopoulos K.E.,Vrahatis M.Particle Swarm Optimization Method in Multiobjective Problems.Proceedings of Symposium on Applied Computing.2002,603-607
[151]Laskari E.C.,Arsopoulos K.E.,Vrahatis M.N.Particle Swarm Optimization for Integer Programming
[152]Guo C.X.,Hu J.S.,Ye B.,Cao Y.J.Swarm Intelligence for Mixed-Variable Design Optimization.Journal of Zhejiang University SCIENCE A.2004,5(7):851-860
[153]Cagnina L.,Esquivel S.,Gallard R.Particle Swarm Optimization for Sequencing Problems:A Case Study.Congress on Evolutionary Computation.USA,2004,536-541
[154]夏蔚军,吴智铭.基于混合微粒群优化的多目标柔性Job-shop调度.控制与决策.2005,28(6):137-141
[155]Salman A.,Aunad L.Particle Optimization for Task Assignment Problem Microprocessors and Microsystems.2002:363-371
[156]Wang K.P.,Huang L.,Zhou C.G.,Pang W.Particle Optimization for Traveling Salesman problem.Proceedings of the Seond International Conference on Machine Learning and Cybernetics.Xi'an,2003,1583-1585
[157]Shi Y.H.,Xing X.L.,et al.A Discrete PSO Method for Generalized TSP Problem.Proceeings of the Third International Conference on Machine Learning and Cybernitics.Shanghai,2004,2378-2383
[158]高海兵,周驰,高亮.广义粒子群优化模型.计算机学报.2005,28(12):1980—1987
[159]Chen Ai-ling,Yang Gen-ke,Wu Zhi-ming.Hybrid Discrete Particle Swarm optimization algorithm for Capacitated Vehicle Routing Problem.Journal of Zhejiang University SCIENCE A.2006
[160]Wu F.,Chandler P.R.,Sparks A.,Banda S.Autonomous Munitions Cooperative Classification.American Institute of Aeronautics and Astronatutics
[161]Mahamedand O.G.H.,Engelbrecht A.P,Salma A.Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification.TRANSACTIONS ON ENGINEERING,COMPUTING AND TECHNOLOGY.2005:199-204
[162]Hu X.H.,Eberhart R.C.,Shi Y.H.Swarm Intelligence for Permutation Optimization:A Case Study of n-Queens Problem.IEEE Swarm Intelligence Symposium.Indianapolis,2003,243-246
[163]Fukuyama Y.Fnndamentals of Particle Swarm Techniquesn.2002
[164]钟一文.智能优化方法及其应用研究.Ph.D.thesis,浙江大学,杭州,中围,2005
[165]Kennedy J.,Eberhart R.C.A Discrete Binary Version of the Particle Swarm Algorithm.Proceedings the World Multiconference on Systemics,Cybernetics and Informatics.Indianapolis,Indiana,1997,4104-4109
[166]Clerc M.Discrete Particle Swarm Optimization,Illustrated by the Traveling Salesman Problem,2000.[Online].http://www.mauricecierc.net/
[167]Thiemo K.,Jakob S.Particle Swarm Optimization Optimization with Spatial Particle Externsion.Proceedings of the Conference on Evolutionary Computation.Honolulu,HI,2002,1474-1479
[168]赵波,郭创新,张鹏翔,曹一家.基于分布式协同粒子群优化算法的电力系统无功优化.中国电机工程学报.2005,25(21)
[169]WebVRP,2000.[Online].http://neo.lcc.uma.es/radi-aeb/
[170]肖健梅,李军军,王锡淮.求解车辆路径问题的改进微粒群优化算法.计算机集成制造系统.2005,11(4):577-581
[171]De Jong K.A.Potter M.A.A Cooperative Coevolutionary Approach to Function Optimization.The Third Parallel Problem Solving From Nature.Jerusalem,Israel,1994,249-257
[172]方开泰,马兴长.正交与均匀试验设计.科学出版社,2001
[173]任露泉.试验优化设计与分析.高等教育出版社,2003
[174]Zhang Chun-gang,Xi Yu-geng.Robot Rolling Path Planning Based on Locally Detected Information.自动化学报.2003,29(1):38-44
[175]肖人彬,王磊.人工免疫系统:原理、模型、分析及展望.计算机学报.2002,25(12):1281—1293
[176]罗印升,李人厚.基于免疫原理的进化算法.控制与决策.2004,19(2):119—222