基于模糊—进化理论的帆船运动路线规划研究
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摘要
帆船在海上行驶是一个极其复杂的过程,要受到海浪、海风及海流等环境条件变化的影响,是一个具有复杂环境、不完备信息的系统。帆船比赛是运动员驾驶帆船在规定的距离内比赛航速的一项运动。如何依据比赛时的海洋气象信息制定最优航行轨迹是比赛取胜的重要环节。利用现代信息技术和智能控制方法规划最优航行路线,指导帆船运动项目的科学训练,提高运动员成绩,促进复杂系统的理论研究,是一项具有实际应用和科研价值的课题。
从优化理论的角度来看,帆船运动路线的规划是一个时变、非线性、受约束、不确定性系统的优化问题,包括帆船力学特性、航行策略、航向控制、航向决策、路线规划等一系列相互关联的问题。
针对以上问题,本文在国家自然科学基金项目“基于模糊-进化理论的奥运帆船项目路线规划方法的研究”的资助下,展开基于模糊-进化理论的帆船运动路线规划的研究。具体的研究内容包括以下几部分:
1.首先对帆船的船体、风帆的受力、及帆船操纵运动及其干扰信号的数学模型进行了较详细的综述。
2.利用流体力学原理分析风帆、船体的受力情况及帆船运动情况,提出了依据风向变化调整帆转角,选择帆船最佳航向的控制策略。进行帆船行驶控制策略分析。与海浪和海流的干扰力相比,风向和风速的变化对帆船的航向和航速的影响最大。忽略海浪和海流对帆船行驶产生的扰动影响,依据对帆船受力情况及运动情况的分析,建立一定区段内航行时间最短的受约束非线性多元函数。利用约束最优化方法,求解不同风向时航行时间最短的转帆角和航向角,从运算结果中归纳出简单易行的航行策略。所推导出的航速预测公式以及航行控制策略,是后续进行帆船操纵运动智能控制以及帆船运动路线规划研究的必备前提条件。
3.提出了一种帆船操纵运动的模糊自适应控制方法。针对模糊逻辑系统有很强的知识表达能力和逻辑推理能力,但自学习能力比较差,而人工神经网络在自
Like many sporting contests, sailboat racing carries with it a high degree of uncertainty. Since sailboats exploit the wind for their speed, a major source of uncertainty lies in the behavior of the wind. Besides, it has to compensate for stochastic disturbances acting upon it, such as wind, waves, and currents. Sailboat racing is a professional sport requiring the best velocity in giving voyage. So, path planning is an important link for the sailboat racing. To instruct the yachtsmen or yachtswomen selecting an optimal sailing route according to complex marine environments, it becomes an important research topic to develop an ancillary sailboat training system based on modern information technology and intelligent control methods. That system will not only improve the scientific training of sailing race as well as the athletes' scores but also enhance the research of perplexing intelligent system.With a view to optimal theory, optimum path planning for sailboat racing is an optimal question of a variable, nonlinear, and restricted system. It includes several interrelated research problems, such as mathematical models, sailing strategies, heading control, and heading decision of a training sailboat.Supported by the National Natural Science Foundation of China (NSFC), the research topic of this paper has been focused on the study of optimum path-planning algorithms for sailboat racing based on fuzzy logic and evolutionary. The main contributions of this paper include the following aspects:1. The theories of sailboat steering are recapitulated, the disturbance of sailboat steering is analyzed, and the mathematical models of the disturbance are established. All the work above is the basis of fuzzy adaptive controller design and path planning simulation.2. The optimal control strategies for sailboat navigation are proposed for trimming sail and select optimal heading by analyzing forces on the sailboat and movement status. Compare with wind, the applied forces acting upon sailboat of waves
and currents are small. A constrained nonlinear multivariable function on minimum navigation time during a certain sailing distance was put forward neglecting the disturbances caused by waves and currents. Given a different wind angle, the best heading angle and trimming angle of sails were found using optimal method, when the constrained nonlinear multivariable function get its minimum value. Several sailing strategies of optimal routing for sailboats to navigate are summarized, according to the optimal results. All the sailing strategies got of this part work are the essential necessary conditions for fuzzy adaptive controller design and path planning algorithms.3. A new fuzzy adaptive control method based on Sugeno fuzzy model is proposed for the nonlinear and time-variant navigation system of sailboats. It combines good merits of fuzzy logic and neural network. The controller uses Sugeno fuzzy model to divide the nonlinear control system into several local linear models, based on which several local linear controllers are constructed. The global control output can be obtained by combining all the local linear controllers' output. The system has high intelligent and adaptability by using linear neuron network to adjust coefficients of each Sugeno fuzzy model. Simulation results show that this method can efficiently implement autonomous navigation of sailboats. This autopilot of a sailboat will be used to simulate a helmsman in path planning simulation.4. Heading decision modeling for optimal path planning is proposed based on fuzzy logic and comprehensive evaluation concept. It uses dynamic programming method to set up the two-dimension moving information for sailing and puts forward a sailing membership function, describing its position and heading information, which relative to the end point. Moreover, it sets up a comprehensive evaluating function concerning the sailing velocity and the sailing membership function. Last, The optimum heading is obtained by finding the minimum of the comprehensive evaluating function with the limiting condition of sea-route width. The heading decision modeling is the basal theory for path planning.5. Path planning is an important link for the sailboat racing. An optimal path-planning algorithm is proposed for straightway sailing race based on evolutionary programming theory. First, an optimal path-planning algorithm based on dynamic programming is researched. Then, to solve the problem of the low convergence of dynamic programming algorithm, an optimal path-planning algorithm based on evolutionary programming theory is proposed. The evolution programming was
embedded in local search to improve the optimization performance and enhance the fast convergence of dynamic programming algorithm. The heading decision with the restriction of sea—route width is transformed into unconstrained nonlinear programming problem by using the penalty function. By compare the simulation results of two path planning algorithm. It indicated the effectiveness and the applicability of the evolutionary programming-based path-planning algorithm.6. From the engineering realization, a training monitoring system of sailboat racing is proposed. It uses GPS, GIS, wireless data transfer, and serial data transfer technologies to realize the real time detection and monitoring of practical sailing route. Therefore, with the using of this system, the path planning algorithms proposed in this thesis can be utilized to improve the scientific training of sailing race.The conclusions and directions for future research work are discussed in the last chapter of this paper.
从优化理论的角度来看,帆船运动路线的规划是一个时变、非线性、受约束、不确定性系统的优化问题,包括帆船力学特性、航行策略、航向控制、航向决策、路线规划等一系列相互关联的问题。
针对以上问题,本文在国家自然科学基金项目“基于模糊-进化理论的奥运帆船项目路线规划方法的研究”的资助下,展开基于模糊-进化理论的帆船运动路线规划的研究。具体的研究内容包括以下几部分:
1.首先对帆船的船体、风帆的受力、及帆船操纵运动及其干扰信号的数学模型进行了较详细的综述。
2.利用流体力学原理分析风帆、船体的受力情况及帆船运动情况,提出了依据风向变化调整帆转角,选择帆船最佳航向的控制策略。进行帆船行驶控制策略分析。与海浪和海流的干扰力相比,风向和风速的变化对帆船的航向和航速的影响最大。忽略海浪和海流对帆船行驶产生的扰动影响,依据对帆船受力情况及运动情况的分析,建立一定区段内航行时间最短的受约束非线性多元函数。利用约束最优化方法,求解不同风向时航行时间最短的转帆角和航向角,从运算结果中归纳出简单易行的航行策略。所推导出的航速预测公式以及航行控制策略,是后续进行帆船操纵运动智能控制以及帆船运动路线规划研究的必备前提条件。
3.提出了一种帆船操纵运动的模糊自适应控制方法。针对模糊逻辑系统有很强的知识表达能力和逻辑推理能力,但自学习能力比较差,而人工神经网络在自
Like many sporting contests, sailboat racing carries with it a high degree of uncertainty. Since sailboats exploit the wind for their speed, a major source of uncertainty lies in the behavior of the wind. Besides, it has to compensate for stochastic disturbances acting upon it, such as wind, waves, and currents. Sailboat racing is a professional sport requiring the best velocity in giving voyage. So, path planning is an important link for the sailboat racing. To instruct the yachtsmen or yachtswomen selecting an optimal sailing route according to complex marine environments, it becomes an important research topic to develop an ancillary sailboat training system based on modern information technology and intelligent control methods. That system will not only improve the scientific training of sailing race as well as the athletes' scores but also enhance the research of perplexing intelligent system.With a view to optimal theory, optimum path planning for sailboat racing is an optimal question of a variable, nonlinear, and restricted system. It includes several interrelated research problems, such as mathematical models, sailing strategies, heading control, and heading decision of a training sailboat.Supported by the National Natural Science Foundation of China (NSFC), the research topic of this paper has been focused on the study of optimum path-planning algorithms for sailboat racing based on fuzzy logic and evolutionary. The main contributions of this paper include the following aspects:1. The theories of sailboat steering are recapitulated, the disturbance of sailboat steering is analyzed, and the mathematical models of the disturbance are established. All the work above is the basis of fuzzy adaptive controller design and path planning simulation.2. The optimal control strategies for sailboat navigation are proposed for trimming sail and select optimal heading by analyzing forces on the sailboat and movement status. Compare with wind, the applied forces acting upon sailboat of waves
and currents are small. A constrained nonlinear multivariable function on minimum navigation time during a certain sailing distance was put forward neglecting the disturbances caused by waves and currents. Given a different wind angle, the best heading angle and trimming angle of sails were found using optimal method, when the constrained nonlinear multivariable function get its minimum value. Several sailing strategies of optimal routing for sailboats to navigate are summarized, according to the optimal results. All the sailing strategies got of this part work are the essential necessary conditions for fuzzy adaptive controller design and path planning algorithms.3. A new fuzzy adaptive control method based on Sugeno fuzzy model is proposed for the nonlinear and time-variant navigation system of sailboats. It combines good merits of fuzzy logic and neural network. The controller uses Sugeno fuzzy model to divide the nonlinear control system into several local linear models, based on which several local linear controllers are constructed. The global control output can be obtained by combining all the local linear controllers' output. The system has high intelligent and adaptability by using linear neuron network to adjust coefficients of each Sugeno fuzzy model. Simulation results show that this method can efficiently implement autonomous navigation of sailboats. This autopilot of a sailboat will be used to simulate a helmsman in path planning simulation.4. Heading decision modeling for optimal path planning is proposed based on fuzzy logic and comprehensive evaluation concept. It uses dynamic programming method to set up the two-dimension moving information for sailing and puts forward a sailing membership function, describing its position and heading information, which relative to the end point. Moreover, it sets up a comprehensive evaluating function concerning the sailing velocity and the sailing membership function. Last, The optimum heading is obtained by finding the minimum of the comprehensive evaluating function with the limiting condition of sea-route width. The heading decision modeling is the basal theory for path planning.5. Path planning is an important link for the sailboat racing. An optimal path-planning algorithm is proposed for straightway sailing race based on evolutionary programming theory. First, an optimal path-planning algorithm based on dynamic programming is researched. Then, to solve the problem of the low convergence of dynamic programming algorithm, an optimal path-planning algorithm based on evolutionary programming theory is proposed. The evolution programming was
embedded in local search to improve the optimization performance and enhance the fast convergence of dynamic programming algorithm. The heading decision with the restriction of sea—route width is transformed into unconstrained nonlinear programming problem by using the penalty function. By compare the simulation results of two path planning algorithm. It indicated the effectiveness and the applicability of the evolutionary programming-based path-planning algorithm.6. From the engineering realization, a training monitoring system of sailboat racing is proposed. It uses GPS, GIS, wireless data transfer, and serial data transfer technologies to realize the real time detection and monitoring of practical sailing route. Therefore, with the using of this system, the path planning algorithms proposed in this thesis can be utilized to improve the scientific training of sailing race.The conclusions and directions for future research work are discussed in the last chapter of this paper.
引文
[1] Richard G. J. Flay. A twisted flow wind tunnel for testing yacht sails. Journal of Wind Engineering and Industrial Aerodynamics 1996 (63): 171-182
[2] R. G. J. Flay, I. J. Vuletich. Development of a wind tunnel test facility for yacht aerodynamic studies. Journal of wind engineering and industrial aerodynamics, 1995 (58): 231-258.
[3] Larsson, L. Scientific Methods in Yacht Design. Annual Review of Fluid Mechanics, 1990(22): 349-385.
[4] Takeshi Sugimoto. Optimum sail design for small heel and weak wind shear conditions. Fluid dynamics Research, 1995 (15): 75-88.
[5] S. P. Piddes, J. H. Gaydon. A new vortex lattice method for calculating the flow past yacht sails. Journal of Wind Engineering and Industrial Aerodynamics, 1996 (63): 35-59
[6] A. H. Day. Sail optimization for maximal speed. Journal of Wind Engineering and Industrial Aerodynamics 63 (1996): 131-154.
[7] K. L. Hedges, P. J. Richards. Computer modeling of downwind sails. Journal of wind engineering and industrial aerodynamics, 1996 (63): 95-110.
[8] Yann Roux, Serge Huberson, etc. Yacht performance prediction: towards a numerical VPP. High Performance Yacht Design Conference. Aucklang, 4-6, Dec. 2002.
[9] Daniel Hartrick Harris. Simulation of upwind maneuvering of a sailing yacht. ME Thesis.
[10] YANG Cheng-en, BI Ying-jun, XIAO Cheng-mo. H_∞ controller with feedforward for yacht course keeping. Journal of Dalian Maritime University, 2002, Vol. 28. Suppl.
[11] J. Gerritsma, J. A. Keuning, R. onnink. Sailing yacht performance in clam water and in waves. 12th International symposium on yacht design and construction, HISWA, Nover, 1992, P925-960.
[12] Yang Yansheng, Zhou Changjiu, Ren jusheng. Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems. Applied Soft Computing Journal, 2003, 3(4): 305-316.
[13] Dougal Harris, Giles Thomas, Martin Renilson. Dowind Performance of Yachts in Waves. 2nd Australian Sailing Science Conference, Feb. 1999. Hobart, Tasmania
[14] 肖成模,杨承恩,Paul Austin.小型帆船自适应自动舵.中国造船,2003,44(1):75-79.
[15] 缪国平.帆船运动的力学原理.力学与实践,1994,16(1):9-18.
[16] Andy Phipott, Andrew Mason. Advances in optimization in yacht performance analysis. High performance yacht design conference Auckand, 4-6 December, 2002
[17] 杨承恩,毕英君.用神经网络控制器对小型帆船的航向控制.中国造船,2004,45(1):25-32.
[18] Farhan A. Faruqi. Non-linear mathematical model for integrated global positioning/inertial navigation systems. Applied Mathematics and Computation, 2000(115): 191-212.
[19] Jasmin Velagic, Zoran Vukiv, Edin Omerdic. Adaptive fuzzy ship autopilot for track keeping. Control Engineering Practice 2003, (11): 433-443.
[20] M. Sandler, A. Wahl, R. Zimmermann, et. Autonomous guidance of ships on waterways. Robotics and Autonomous Systems. 1996 (18): 327-335.
[21] K. Ohtsu, K. Shoji, T. Okazaki. Minimun-time Maneuvering of A Ship with Wind Disturbances. Control Eng. Practice, Vol. 4, No. 3, pp. 385-392, 1996
[22] Edward Tunstel, H. Danny, T. Lippincott, M. Jamshidi. Autonomous Navigation using an Adaptive Hierarchy of Multiple Fuzzy-Behaviors. Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA'97).
[23] K. D. Do, Z. P. Jiang, J. Pan. Robust adaptive path following of underactuated ships. Proceedings of the 41st IEEE Conference on Decision and Control Las Vegas, Nevada USA, December 2002
[24] Yoon, Hyeon Kyu; Rhee, Key Pyo. Identification of hydrodynamic coefficients in ship maneuvering equations of motion by Estimation-Before-Modeling technique. Ocean Engineering, 2003, 30(18): 2379-2404.
[25] Buchan, A. L., Flewitt, J. R., The behaviour and stability of wind operated steering systems for sailing yachts. Transactions of the Royal Institution of Naval Architects (RINA), 1968.
[26] Letcher, J. S. Selfsteering for sailing craft. International Marine, USA, 1974.
[27] Curtiss, H. C. On the stability an control of sailing yachts, Ancient Interface2. AIAA Symposium on the Aero/Hydronautics of sailing, AIAA, 1970.
[28] Titlow, J. D., a dynamic model for downwind sailing. Ancient Interface. AIAA Symposium on the Aero/Hydronautics of sailing, AIAA, 1977.
[29] P. S. Jackson. Modelling the aerodynamics of upwind sails. Journal of Wind Engineering and Industrial Aerodynamics 63(1996): 17-34. Nomoto, K., Tatano, H., balance of helm of sailing yachts, Symposium yachtarchitecture' 79, HISWA, 1979.
[30] R. Sullivan. Yacht velocity prediction, ME Thesis, The university of Auckland, 1989.
[31] David Teirney. Yacht match race simulation. Twelfth Australasian Fluid Mechanics Conference, Sydney, Australia, 1995.
[32] Wallis, B. D., Pourzanjani, M. A., a six degree of freedom model for small vessels. Manoeuvring and Control of Marine Craft: Proceedings of Second International Conference, Southampton, UK, 1992.
[33] Browning, A. a mathematical model to simulate small boat behaviour, Simulation, Simulation Councils, Inc, 1991
[34] Akimoto, H., finite volume simulation of the flow around a sailing boat with unsteady motion. Journal of the Society of Naval Architects of Japan, 1997.
[35] Xiao C. M., and Austin, P. C., yacht modeling and adaptive control, IFAC Conference on Manoeuvring and Control of Marines Craft, IFAC, Aalborg, Denmark, 2000.
[36] Oxnam, G., simulated sailing, Sailing World, December 1996.
[37] Andrews, L. T., and Klingler, J. W., a sailing simulator, Ancient Interface 14, AIAA Symposium on the Aero/Hydronautics of sailing, AIAA, 1984.
[38] Amerongen J V. Recent developments in automatic steering of ships. Navigation, 1986, 39(3): 349-362
[39] Vukic Z, Omerdie E, Kujaka L., Improved fuzzy autopilot for track-keeping, Proc. IFAC Conf. CAMS'98, Fukuoka, Japan, 1998:135-140.
[40] Endo, M. Amerongen J. van and Bakkers, A. W. P., Applicability of Neural networks to ship steering, CAMS'89: 221-232.
[41] Roland S. Burns. The use of artificial neural networks for the intelligent optimal control of surface ships, IEEE J. Oceanic Engineering, 1995, 20(1): 65-72.
[42] C T Lin and C S G Lee. Neural-network-based fuzzy logic control and decision system. IEEE Trans. Comput. 1991:1320-1336.
[43] 郭晨,苏辉,杨国勋.基于GA-FCMAC算法的船舶运动智能控制器.中国航海,2002,1:12-15.
[44] 张化光,何希勤.模糊自适应控制理论及其应用.北京:北京航空航天大学出版社,2002.
[45] 李士勇著.模糊控制、神经控制和智能控制论.哈尔滨:哈尔滨工业大学出版社,1998
[46] Sutton R. A design study of a self-organizing fuzzy autopilot for ship control. In: Proc Instn Mech Engrs (pt, Ⅰ), 1991, 20(5): 35-47
[47] Jeffery R L. Fuzzy model reference learning control for cargo ship steering. IEEE Control System, 1993, 13(5): 23-24
[48] Layne J.R., Paraons K., Fuzzy model reference control for cargo ship steering. IEEE Trans Control Syst. Mag., 1993, (13): 23-34.
[49] Parsons M.G., Chubb A.C., Cao Y.S., an assessment of fuzzy logic vessel path control, IEEE J of Ocean Engineering, 1995,20(4): 276-284.
[50] 阎平凡,张长水.人工神经网络与模拟进化计算.清华大学出版社,2000.
[51] Zhang Y, Hearn G.E., Sen P. An on-line trained adaptive controller, IEEE Control System Magazine, 1995, 15: 67-75.
[52] Zhang Y, Heam G.E., Sen P. A neural network approach to ship tack keeping control, IEEE J. Oceanic Engineering, 1996, 21:513-527.
[53] Zhang Y. The study of neural networks and its applications to ship modeling and control, Ph.D Thesis, Uni. Of Newcastle upon Tyne, 1997.
[54] 潘正君,康立山.演化计算.清华大学出版社,1998:1-15.
[55] 陈小平.进化计算及其应用研究.南京航空航天大学博士学位论文,2000:4-6.
[56] 孟庆春.遗传算法及其应用.济南:山东大学出版社,1995:81-88
[57] M.Y. El-Sharkh, A.A. El-Keib, H. Chen. A fuzzy evolutionary programming-based solution methodology for security-constrained generation maintenance scheduling. Electric Power Systems Research 2003(67): 67-72.
[58] F.Jimenez, J.M. Cadenas, J.L.Verdegay, G..Sanchez. Solving fuzzy optimization problems by evolutionary algorithms. Information Sciences, 2003(152): 303-311.
[59] Kim Jong-Hwan, Kim Kwang-Choom. Multicriteria Fuzzy Control Using Evolutionary Programming, Information Sciences. 1997,103: 71-86.
[60] Euan W. McGookin, David J. Murray-Smith, Ship steering control system optimization using genetic algorithms. Control Engineering Practice 2000(8): 429-443.
[61] Masayuma, Y., Fukasawa, T., and Sasagawa, H., tacking simulation of a sailing yacht-numerical integration of equations of motion and application of neural network technique. 12th Chesapeake Sailing Yacht Symposium, SNAME, 1995.
[62] Ching-Yu Tyan, Paul P.Wang, Dennis R.Bahler. An application on intelligent control using neural network and fuzzy logic. Neurocomputing 1996, (12): 345-363.
[63] Sutton, R., Roberts.G.N, & Taylar, S.D. Tuning fuzzy ship autopilots using artificial neural network. Journal of Transactions of the Institute of Measurement and Control, 1997,19(2): 94-106.
[64] 杨国勋,郭晨,贾欣乐.基于增强型学习算法的船舶运动混合智能控制.中国航海,2001,2:1-5.
[65] 贾欣乐,张显库著.船舶运动智能控制与H_∞鲁棒控制.大连:大连海事大学出版社,2002:15-42.
[66] 陆志才著.船舶操纵.大连:大连海事大学出版社,2000.
[67] 刘清.船舶操纵运动模糊神经网络控制系统研究.武汉理工大学博士学位论文,2002:9-11.
[68] Fogel D B. An introduction to simulated evolutionary optimization. IEEE Trans on Neural Networks, 1994: 5(1): 3-13.
[69] Fogel D B. Continuous evolutionary programming: analysis and experiments. Cybernetics and Systems, 1995: 26(1): 79-90.
[70] 藏传德.船舶航行德智能预测控制研究.哈尔滨工程大学博士学位论文,2002。
[71] Kallstrom C G. Identification and adaptive control applied to ship steering, Ph.D.Thesis, Lund Institute of Technology, Lund, Sweden, 1982.
[72] 王平等译.流体力学大全.北京:北京航空航天大学出版社,1991.
[73] A.R.Claughton, M.R.I.N.A., etc. Wind tunnel testing of sailing yacht rigs, proceedings o the 13th international HISWA symposium on yacht design and yacht construction, 1994: 89-106.
[74] 陈宝林著.最优化理论与方法.北京:清华大学出版社,1989:229-235.
[75] Mohand Mokhtari, Michel Marie.MATLAB与SIMULINK工程应用.电子工业出版社,2002.
[76] 葛艳,孟庆春,闫传军等.帆船行驶最优控制策略分析.第23届中国控制会议,2004.8.413-417.
[77] 葛艳,孟庆春,魏振刚等.基于Sugeno模糊模型的帆船控制方法研究.控制与决策,2004.19(6):659-662.
[78] A.EI Hajjaji, S.Bentalba. Fuzzy path tracking control for automatic steering of vehicles. Robotics and Autonomous Systems, 2003, (43): 203-213.
[79] Kevin M.Passino, Stephen Yurkovich. Fuzzy Control. 北京:清华大学出版社, 2001.
[80] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans.on SMC, 1985,15(1): 116-132.
[81] Shaocheng Tong, Tao Wang, Han-Xiong Li. Fuzzy robust tracking control for uncertain nonlinear systems. International Journal of approximate reasoning, 2002(30): 73-90.
[82] M.N.Polkinghorne, G.N.Roberts, R.S.Burns and D.Winwood.The implementation of fixed rulebase fuzzy logic to the control of small surface ships. Control Engineering Practice, 1995, 3(3): 321-328.
[83] 从爽.神经网络、模糊系统及其在运动控制中的应用.安徽:中国科学技术大学出版社,2002,110-134.
[84] Vukic.Z.,&Velagic.J. Comparative analysis of Mamdani and Sugeno type fuzzy autopilots for ships. CD Proceedings of the Fifth European Control Conference, 1999 (Paper No.F0505).Karlsruhe,Germany.
[85] A.Homaifar, M.Bikdash, C.Clifton. Approximating an optimal feedback control law by a generalized Sugeno controller. Fuzzy Sets and System, 2001 (121): 39-57.
[86] Thierry Marie Guerra, Laurent Vermeiren. Control laws for Takagi-Sugeno fuzzy models. Fuzzy Sets and System, 2001(120): 95-108.
[87] Felipe Fernandez, Julio Gutierrez. A Takagi-Sugeno model with fuzzy inputs viewed from multidimensional interval analysis. Fuzzy Sets and Systems, 2003(135): 39-61.
[88] K.Djouani,Y.Hamam. Feedback optimal neural network controller for dynamic systems - A ship-maneuvering example, Mathematics and Computers in Simulation 1996, (41): 117-127.
[89] Skjetne Roger, Fossen Thor, KokotoviAA Petar V. Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory. Automatica, 2005, 41(2): 289-298.
[90] Moenes Iskarous, Kazuhiko Kawamura. Intelligent control using a neuro-fuzzy nerwork. IEEE Proceedings of the international conference on intelligent robots and systems (IROS'95),, 1995: 350-355.
[91] Biswajit Paul, Amit Konar, Ajit K.Mandal. Fuzzy ADALINEs for gray image recognition. Neurocomputing, 1999(24): 207-223.
[92] Gonzalo Pajares, Jesus M.de la Cruz. Local stereovision matching through the ADALINE neural network. Pattern Recognition Letters 2001, (22): 1457-1473.
[93] S.Hui, S.H. Zak. The Widrow-Hoff algorithm for McCulloch-Pitts-type neuron. IEEE Trans. Neural Networks, 1994,5(6): 924-929.
[94] 庄晓东,孟庆春,殷波等.动态环境中基于模糊概念的机器人路径搜索方法.机器人,2001,23(5):397-458.
[95] Azaron, Amir, Kianfar Farhad. Dynamic shortest path in stochastic dynamic networks: Ship routing problem. European Journal of Operational Research, 2003,144(1): 138-156.
[96] Fagerholt Kjetil, Christiansen Marielle. A travelling salesman problem with allocation, time window and precedence constraints —an application to ship scheduling. International Transactions in Operational Research, 2000, 7(3): 231-244.
[97] Carlos F. Daganzo. Reversibility of the time-dependent shortest path problem. Transportation Research Part B, 2002 (36): 665-668
[98] Abdolhamid Torki, Samerkae Somhon and Takao Enkawa. A Competitive Neural Network Algorithm for solving vehicle Routing Problem. Computers ind. Enging 1997, 33(3): 473-476.
[99] 邵世明,姜次平.船舶阻力.上海:上海交通大学出版社,1987.
[100] 卫翔,郁振伟,王智勇.基于动态规划的舰船最佳航路设计.中国航海,2003,57(4):16-18.
[101] 李丽娜.航海自动化.北京:人民交通出版社,2000.
[102] Y.Nakamura, Y.Miyamoto, and T.Hayashi. Measurement of ship heading using DGPS technique (in Japanese). Applied oceanography and engineering, 1995, 33(4): 209-214.
[103] K.D.Do, Z.P.Jiang, J.Pan. Universal controllers for stabilization and tracking of under actuated ships. Systems & Control Letters, 2002(47): 299-317.
[104] Rycroft,M.J., Understanding GPS. Principles and Applications. Journal of Atmospheric and Solar-Terrestrial Physics, 1997, 59(5): 598-599.
[105] Pavlis, E.C., Comparison ofGPS S/C orbits determined from GPS and SLR tracking data. Advances in Space Research, 1995, 16(12): 1255-1258.
[106] R.E.Huss, J.W.Weber, Routing finding digital terrain data, IEEE/AIAA 5th digital avionics system conferfence, 1983: 1441-1444.
[107] 李筱菁,孟庆春.GPS技术在城市交通状况实时检测技术中的应用.青岛海津大学学报,2002,32(3):475-481.
[108] Mattauer, Maurice, New GPS data in China: a key for a better understanding of the Cainozoic tectonics of Asia. C omptes Rendus Geosciences, 2002, 334(11): 809-810.
[109] 张静,钟章队.GSM中的通用分组无线业务—GPRS.电信技术,2000(2):9-13.
[110] 张正恒,张其善.基于GPRS的车载信息平台的研制与关键技术.北京航空航天大学学报,2005,31(1):98-101.
[111] 黄宇.GSM网络通信在车载GPS定位管理系统中的应用.中国数据通信,2003(11):30-33.
[112] 李民.Ⅱ型海洋资料浮标接收岸站设计.海洋技术,1998,17(2):34-37.
[113] 卜照蓬,刘岩.FZF3-1型海洋资料浮标系统.海洋技术,2003,22(2):59-65.
[114] N.Ward, R. Johannessen. Interference to GPS in the marine environment. Journal of navigation, 1996, 49(2): 210-218.
[115] McNemey, Michael Thomas, The use of geographical information systems for airport engineering and management. Transportation Research Part A: Policy and Practice, 1997,31(1), pp.58
[116] Lee, Jay. Managing probation programs with geographic information systems. Computers, Environment and Urban Systems, 1995,19(5-6): 409-417.
[117] Cox, Allan B. An overview to geographic information systems. The Journal of Academic Libratianship, 1995, 21(4): 237-249.
[118] 谢仕义.基于MapX的COM GIS技术的研究及实现.计算机应用研究,2003(5):51-54.
[119] Rosen Scott L., Harmonsky Cahterine M. An improved simulated annealing simulation optimization method for discrete parameter stochastic systems. Computers and Operations Research, 2005, 32(2): 343-385.
[120] Hyun Myung, Jong-Hwan Kim. Hybrid evolutionary programming for heavily constrained problems. Biosystems, 1996, 38(1): 29-43.
[121] Vitanyi, Pual. A discipline of evolutionary programming. Theoretical Computer Science, 2000, 241(1-2): 3-23.
[2] R. G. J. Flay, I. J. Vuletich. Development of a wind tunnel test facility for yacht aerodynamic studies. Journal of wind engineering and industrial aerodynamics, 1995 (58): 231-258.
[3] Larsson, L. Scientific Methods in Yacht Design. Annual Review of Fluid Mechanics, 1990(22): 349-385.
[4] Takeshi Sugimoto. Optimum sail design for small heel and weak wind shear conditions. Fluid dynamics Research, 1995 (15): 75-88.
[5] S. P. Piddes, J. H. Gaydon. A new vortex lattice method for calculating the flow past yacht sails. Journal of Wind Engineering and Industrial Aerodynamics, 1996 (63): 35-59
[6] A. H. Day. Sail optimization for maximal speed. Journal of Wind Engineering and Industrial Aerodynamics 63 (1996): 131-154.
[7] K. L. Hedges, P. J. Richards. Computer modeling of downwind sails. Journal of wind engineering and industrial aerodynamics, 1996 (63): 95-110.
[8] Yann Roux, Serge Huberson, etc. Yacht performance prediction: towards a numerical VPP. High Performance Yacht Design Conference. Aucklang, 4-6, Dec. 2002.
[9] Daniel Hartrick Harris. Simulation of upwind maneuvering of a sailing yacht. ME Thesis.
[10] YANG Cheng-en, BI Ying-jun, XIAO Cheng-mo. H_∞ controller with feedforward for yacht course keeping. Journal of Dalian Maritime University, 2002, Vol. 28. Suppl.
[11] J. Gerritsma, J. A. Keuning, R. onnink. Sailing yacht performance in clam water and in waves. 12th International symposium on yacht design and construction, HISWA, Nover, 1992, P925-960.
[12] Yang Yansheng, Zhou Changjiu, Ren jusheng. Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems. Applied Soft Computing Journal, 2003, 3(4): 305-316.
[13] Dougal Harris, Giles Thomas, Martin Renilson. Dowind Performance of Yachts in Waves. 2nd Australian Sailing Science Conference, Feb. 1999. Hobart, Tasmania
[14] 肖成模,杨承恩,Paul Austin.小型帆船自适应自动舵.中国造船,2003,44(1):75-79.
[15] 缪国平.帆船运动的力学原理.力学与实践,1994,16(1):9-18.
[16] Andy Phipott, Andrew Mason. Advances in optimization in yacht performance analysis. High performance yacht design conference Auckand, 4-6 December, 2002
[17] 杨承恩,毕英君.用神经网络控制器对小型帆船的航向控制.中国造船,2004,45(1):25-32.
[18] Farhan A. Faruqi. Non-linear mathematical model for integrated global positioning/inertial navigation systems. Applied Mathematics and Computation, 2000(115): 191-212.
[19] Jasmin Velagic, Zoran Vukiv, Edin Omerdic. Adaptive fuzzy ship autopilot for track keeping. Control Engineering Practice 2003, (11): 433-443.
[20] M. Sandler, A. Wahl, R. Zimmermann, et. Autonomous guidance of ships on waterways. Robotics and Autonomous Systems. 1996 (18): 327-335.
[21] K. Ohtsu, K. Shoji, T. Okazaki. Minimun-time Maneuvering of A Ship with Wind Disturbances. Control Eng. Practice, Vol. 4, No. 3, pp. 385-392, 1996
[22] Edward Tunstel, H. Danny, T. Lippincott, M. Jamshidi. Autonomous Navigation using an Adaptive Hierarchy of Multiple Fuzzy-Behaviors. Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA'97).
[23] K. D. Do, Z. P. Jiang, J. Pan. Robust adaptive path following of underactuated ships. Proceedings of the 41st IEEE Conference on Decision and Control Las Vegas, Nevada USA, December 2002
[24] Yoon, Hyeon Kyu; Rhee, Key Pyo. Identification of hydrodynamic coefficients in ship maneuvering equations of motion by Estimation-Before-Modeling technique. Ocean Engineering, 2003, 30(18): 2379-2404.
[25] Buchan, A. L., Flewitt, J. R., The behaviour and stability of wind operated steering systems for sailing yachts. Transactions of the Royal Institution of Naval Architects (RINA), 1968.
[26] Letcher, J. S. Selfsteering for sailing craft. International Marine, USA, 1974.
[27] Curtiss, H. C. On the stability an control of sailing yachts, Ancient Interface2. AIAA Symposium on the Aero/Hydronautics of sailing, AIAA, 1970.
[28] Titlow, J. D., a dynamic model for downwind sailing. Ancient Interface. AIAA Symposium on the Aero/Hydronautics of sailing, AIAA, 1977.
[29] P. S. Jackson. Modelling the aerodynamics of upwind sails. Journal of Wind Engineering and Industrial Aerodynamics 63(1996): 17-34. Nomoto, K., Tatano, H., balance of helm of sailing yachts, Symposium yachtarchitecture' 79, HISWA, 1979.
[30] R. Sullivan. Yacht velocity prediction, ME Thesis, The university of Auckland, 1989.
[31] David Teirney. Yacht match race simulation. Twelfth Australasian Fluid Mechanics Conference, Sydney, Australia, 1995.
[32] Wallis, B. D., Pourzanjani, M. A., a six degree of freedom model for small vessels. Manoeuvring and Control of Marine Craft: Proceedings of Second International Conference, Southampton, UK, 1992.
[33] Browning, A. a mathematical model to simulate small boat behaviour, Simulation, Simulation Councils, Inc, 1991
[34] Akimoto, H., finite volume simulation of the flow around a sailing boat with unsteady motion. Journal of the Society of Naval Architects of Japan, 1997.
[35] Xiao C. M., and Austin, P. C., yacht modeling and adaptive control, IFAC Conference on Manoeuvring and Control of Marines Craft, IFAC, Aalborg, Denmark, 2000.
[36] Oxnam, G., simulated sailing, Sailing World, December 1996.
[37] Andrews, L. T., and Klingler, J. W., a sailing simulator, Ancient Interface 14, AIAA Symposium on the Aero/Hydronautics of sailing, AIAA, 1984.
[38] Amerongen J V. Recent developments in automatic steering of ships. Navigation, 1986, 39(3): 349-362
[39] Vukic Z, Omerdie E, Kujaka L., Improved fuzzy autopilot for track-keeping, Proc. IFAC Conf. CAMS'98, Fukuoka, Japan, 1998:135-140.
[40] Endo, M. Amerongen J. van and Bakkers, A. W. P., Applicability of Neural networks to ship steering, CAMS'89: 221-232.
[41] Roland S. Burns. The use of artificial neural networks for the intelligent optimal control of surface ships, IEEE J. Oceanic Engineering, 1995, 20(1): 65-72.
[42] C T Lin and C S G Lee. Neural-network-based fuzzy logic control and decision system. IEEE Trans. Comput. 1991:1320-1336.
[43] 郭晨,苏辉,杨国勋.基于GA-FCMAC算法的船舶运动智能控制器.中国航海,2002,1:12-15.
[44] 张化光,何希勤.模糊自适应控制理论及其应用.北京:北京航空航天大学出版社,2002.
[45] 李士勇著.模糊控制、神经控制和智能控制论.哈尔滨:哈尔滨工业大学出版社,1998
[46] Sutton R. A design study of a self-organizing fuzzy autopilot for ship control. In: Proc Instn Mech Engrs (pt, Ⅰ), 1991, 20(5): 35-47
[47] Jeffery R L. Fuzzy model reference learning control for cargo ship steering. IEEE Control System, 1993, 13(5): 23-24
[48] Layne J.R., Paraons K., Fuzzy model reference control for cargo ship steering. IEEE Trans Control Syst. Mag., 1993, (13): 23-34.
[49] Parsons M.G., Chubb A.C., Cao Y.S., an assessment of fuzzy logic vessel path control, IEEE J of Ocean Engineering, 1995,20(4): 276-284.
[50] 阎平凡,张长水.人工神经网络与模拟进化计算.清华大学出版社,2000.
[51] Zhang Y, Hearn G.E., Sen P. An on-line trained adaptive controller, IEEE Control System Magazine, 1995, 15: 67-75.
[52] Zhang Y, Heam G.E., Sen P. A neural network approach to ship tack keeping control, IEEE J. Oceanic Engineering, 1996, 21:513-527.
[53] Zhang Y. The study of neural networks and its applications to ship modeling and control, Ph.D Thesis, Uni. Of Newcastle upon Tyne, 1997.
[54] 潘正君,康立山.演化计算.清华大学出版社,1998:1-15.
[55] 陈小平.进化计算及其应用研究.南京航空航天大学博士学位论文,2000:4-6.
[56] 孟庆春.遗传算法及其应用.济南:山东大学出版社,1995:81-88
[57] M.Y. El-Sharkh, A.A. El-Keib, H. Chen. A fuzzy evolutionary programming-based solution methodology for security-constrained generation maintenance scheduling. Electric Power Systems Research 2003(67): 67-72.
[58] F.Jimenez, J.M. Cadenas, J.L.Verdegay, G..Sanchez. Solving fuzzy optimization problems by evolutionary algorithms. Information Sciences, 2003(152): 303-311.
[59] Kim Jong-Hwan, Kim Kwang-Choom. Multicriteria Fuzzy Control Using Evolutionary Programming, Information Sciences. 1997,103: 71-86.
[60] Euan W. McGookin, David J. Murray-Smith, Ship steering control system optimization using genetic algorithms. Control Engineering Practice 2000(8): 429-443.
[61] Masayuma, Y., Fukasawa, T., and Sasagawa, H., tacking simulation of a sailing yacht-numerical integration of equations of motion and application of neural network technique. 12th Chesapeake Sailing Yacht Symposium, SNAME, 1995.
[62] Ching-Yu Tyan, Paul P.Wang, Dennis R.Bahler. An application on intelligent control using neural network and fuzzy logic. Neurocomputing 1996, (12): 345-363.
[63] Sutton, R., Roberts.G.N, & Taylar, S.D. Tuning fuzzy ship autopilots using artificial neural network. Journal of Transactions of the Institute of Measurement and Control, 1997,19(2): 94-106.
[64] 杨国勋,郭晨,贾欣乐.基于增强型学习算法的船舶运动混合智能控制.中国航海,2001,2:1-5.
[65] 贾欣乐,张显库著.船舶运动智能控制与H_∞鲁棒控制.大连:大连海事大学出版社,2002:15-42.
[66] 陆志才著.船舶操纵.大连:大连海事大学出版社,2000.
[67] 刘清.船舶操纵运动模糊神经网络控制系统研究.武汉理工大学博士学位论文,2002:9-11.
[68] Fogel D B. An introduction to simulated evolutionary optimization. IEEE Trans on Neural Networks, 1994: 5(1): 3-13.
[69] Fogel D B. Continuous evolutionary programming: analysis and experiments. Cybernetics and Systems, 1995: 26(1): 79-90.
[70] 藏传德.船舶航行德智能预测控制研究.哈尔滨工程大学博士学位论文,2002。
[71] Kallstrom C G. Identification and adaptive control applied to ship steering, Ph.D.Thesis, Lund Institute of Technology, Lund, Sweden, 1982.
[72] 王平等译.流体力学大全.北京:北京航空航天大学出版社,1991.
[73] A.R.Claughton, M.R.I.N.A., etc. Wind tunnel testing of sailing yacht rigs, proceedings o the 13th international HISWA symposium on yacht design and yacht construction, 1994: 89-106.
[74] 陈宝林著.最优化理论与方法.北京:清华大学出版社,1989:229-235.
[75] Mohand Mokhtari, Michel Marie.MATLAB与SIMULINK工程应用.电子工业出版社,2002.
[76] 葛艳,孟庆春,闫传军等.帆船行驶最优控制策略分析.第23届中国控制会议,2004.8.413-417.
[77] 葛艳,孟庆春,魏振刚等.基于Sugeno模糊模型的帆船控制方法研究.控制与决策,2004.19(6):659-662.
[78] A.EI Hajjaji, S.Bentalba. Fuzzy path tracking control for automatic steering of vehicles. Robotics and Autonomous Systems, 2003, (43): 203-213.
[79] Kevin M.Passino, Stephen Yurkovich. Fuzzy Control. 北京:清华大学出版社, 2001.
[80] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans.on SMC, 1985,15(1): 116-132.
[81] Shaocheng Tong, Tao Wang, Han-Xiong Li. Fuzzy robust tracking control for uncertain nonlinear systems. International Journal of approximate reasoning, 2002(30): 73-90.
[82] M.N.Polkinghorne, G.N.Roberts, R.S.Burns and D.Winwood.The implementation of fixed rulebase fuzzy logic to the control of small surface ships. Control Engineering Practice, 1995, 3(3): 321-328.
[83] 从爽.神经网络、模糊系统及其在运动控制中的应用.安徽:中国科学技术大学出版社,2002,110-134.
[84] Vukic.Z.,&Velagic.J. Comparative analysis of Mamdani and Sugeno type fuzzy autopilots for ships. CD Proceedings of the Fifth European Control Conference, 1999 (Paper No.F0505).Karlsruhe,Germany.
[85] A.Homaifar, M.Bikdash, C.Clifton. Approximating an optimal feedback control law by a generalized Sugeno controller. Fuzzy Sets and System, 2001 (121): 39-57.
[86] Thierry Marie Guerra, Laurent Vermeiren. Control laws for Takagi-Sugeno fuzzy models. Fuzzy Sets and System, 2001(120): 95-108.
[87] Felipe Fernandez, Julio Gutierrez. A Takagi-Sugeno model with fuzzy inputs viewed from multidimensional interval analysis. Fuzzy Sets and Systems, 2003(135): 39-61.
[88] K.Djouani,Y.Hamam. Feedback optimal neural network controller for dynamic systems - A ship-maneuvering example, Mathematics and Computers in Simulation 1996, (41): 117-127.
[89] Skjetne Roger, Fossen Thor, KokotoviAA Petar V. Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory. Automatica, 2005, 41(2): 289-298.
[90] Moenes Iskarous, Kazuhiko Kawamura. Intelligent control using a neuro-fuzzy nerwork. IEEE Proceedings of the international conference on intelligent robots and systems (IROS'95),, 1995: 350-355.
[91] Biswajit Paul, Amit Konar, Ajit K.Mandal. Fuzzy ADALINEs for gray image recognition. Neurocomputing, 1999(24): 207-223.
[92] Gonzalo Pajares, Jesus M.de la Cruz. Local stereovision matching through the ADALINE neural network. Pattern Recognition Letters 2001, (22): 1457-1473.
[93] S.Hui, S.H. Zak. The Widrow-Hoff algorithm for McCulloch-Pitts-type neuron. IEEE Trans. Neural Networks, 1994,5(6): 924-929.
[94] 庄晓东,孟庆春,殷波等.动态环境中基于模糊概念的机器人路径搜索方法.机器人,2001,23(5):397-458.
[95] Azaron, Amir, Kianfar Farhad. Dynamic shortest path in stochastic dynamic networks: Ship routing problem. European Journal of Operational Research, 2003,144(1): 138-156.
[96] Fagerholt Kjetil, Christiansen Marielle. A travelling salesman problem with allocation, time window and precedence constraints —an application to ship scheduling. International Transactions in Operational Research, 2000, 7(3): 231-244.
[97] Carlos F. Daganzo. Reversibility of the time-dependent shortest path problem. Transportation Research Part B, 2002 (36): 665-668
[98] Abdolhamid Torki, Samerkae Somhon and Takao Enkawa. A Competitive Neural Network Algorithm for solving vehicle Routing Problem. Computers ind. Enging 1997, 33(3): 473-476.
[99] 邵世明,姜次平.船舶阻力.上海:上海交通大学出版社,1987.
[100] 卫翔,郁振伟,王智勇.基于动态规划的舰船最佳航路设计.中国航海,2003,57(4):16-18.
[101] 李丽娜.航海自动化.北京:人民交通出版社,2000.
[102] Y.Nakamura, Y.Miyamoto, and T.Hayashi. Measurement of ship heading using DGPS technique (in Japanese). Applied oceanography and engineering, 1995, 33(4): 209-214.
[103] K.D.Do, Z.P.Jiang, J.Pan. Universal controllers for stabilization and tracking of under actuated ships. Systems & Control Letters, 2002(47): 299-317.
[104] Rycroft,M.J., Understanding GPS. Principles and Applications. Journal of Atmospheric and Solar-Terrestrial Physics, 1997, 59(5): 598-599.
[105] Pavlis, E.C., Comparison ofGPS S/C orbits determined from GPS and SLR tracking data. Advances in Space Research, 1995, 16(12): 1255-1258.
[106] R.E.Huss, J.W.Weber, Routing finding digital terrain data, IEEE/AIAA 5th digital avionics system conferfence, 1983: 1441-1444.
[107] 李筱菁,孟庆春.GPS技术在城市交通状况实时检测技术中的应用.青岛海津大学学报,2002,32(3):475-481.
[108] Mattauer, Maurice, New GPS data in China: a key for a better understanding of the Cainozoic tectonics of Asia. C omptes Rendus Geosciences, 2002, 334(11): 809-810.
[109] 张静,钟章队.GSM中的通用分组无线业务—GPRS.电信技术,2000(2):9-13.
[110] 张正恒,张其善.基于GPRS的车载信息平台的研制与关键技术.北京航空航天大学学报,2005,31(1):98-101.
[111] 黄宇.GSM网络通信在车载GPS定位管理系统中的应用.中国数据通信,2003(11):30-33.
[112] 李民.Ⅱ型海洋资料浮标接收岸站设计.海洋技术,1998,17(2):34-37.
[113] 卜照蓬,刘岩.FZF3-1型海洋资料浮标系统.海洋技术,2003,22(2):59-65.
[114] N.Ward, R. Johannessen. Interference to GPS in the marine environment. Journal of navigation, 1996, 49(2): 210-218.
[115] McNemey, Michael Thomas, The use of geographical information systems for airport engineering and management. Transportation Research Part A: Policy and Practice, 1997,31(1), pp.58
[116] Lee, Jay. Managing probation programs with geographic information systems. Computers, Environment and Urban Systems, 1995,19(5-6): 409-417.
[117] Cox, Allan B. An overview to geographic information systems. The Journal of Academic Libratianship, 1995, 21(4): 237-249.
[118] 谢仕义.基于MapX的COM GIS技术的研究及实现.计算机应用研究,2003(5):51-54.
[119] Rosen Scott L., Harmonsky Cahterine M. An improved simulated annealing simulation optimization method for discrete parameter stochastic systems. Computers and Operations Research, 2005, 32(2): 343-385.
[120] Hyun Myung, Jong-Hwan Kim. Hybrid evolutionary programming for heavily constrained problems. Biosystems, 1996, 38(1): 29-43.
[121] Vitanyi, Pual. A discipline of evolutionary programming. Theoretical Computer Science, 2000, 241(1-2): 3-23.