用户名: 密码: 验证码:
非线性多阶段最优控制系统理论、算法及应用
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
本文根据三维水平井井眼轨道设计的实际背景,研究一类非线性多阶段最优控制系统,包括系统的状态方程解的存在性、最优性条件、求解该系统的全局优化算法以及把算法应用到实际的三维水平井井眼轨道设计中。本文的主要研究内容可概括如下:
     1.对一类非线性多阶段最优控制系统,讨论它的状态方程组解的存在性、最优控制系统最优解的存在性及最优性条件。
     2.为了获得非线性多阶段最优控制系统的全局最优解,把该最优控制系统转化为一个非线性规划问题求解。由于该非线性规划问题的目标函数和约束函数可能是隐函数或不可微的,因此给出三种基于随机搜索的全局优化算法。
     (1).把均匀设计、聚类思想与遗传算法结合起来,给出了改进的混合遗传算法解决不带终端约束的非线性多阶段最优控制问题,并进行了收敛性分析。
     (2).针对带有终端约束的非线性多阶段最优控制系统,给出了改进的进化规划算法。在这个算法中,对于目标函数和不同的约束函数,分别在每一点根据两种情况定义两种电荷,基于电磁理论求出合力,把合力方向作为变异搜索方向。为了以较大概率抛弃不可行点,定义一个新的适应性函数。并对算法进行了收敛性分析。
     (3).针对含有等式约束的非线性多阶段最优控制系统,给出了一个全局优化算法。在该算法中,利用凝聚函数构造一个可行集替代目标函数值小于当前目标函数值的可行域,基于随机搜索技术和局部搜索技术寻找该可行集内新的可行点,这个过程直到找不到新的可行点为止。并对算法的收敛性进行了分析。根据此算法的思想给出一个求解含有终端约束的三维水平井轨道设计最优控制系统的全局优化算法。
     3.根据三维水平井轨道控制的特性,建立了以井斜角、方位角、北坐标、东坐标和垂深坐标为状态变量表示井眼轨道曲线的非线性多阶段动力系统(即状态方程组)。基于建立的动力系统,从不同角度考虑,建立了四个三维水平井井眼轨道设计最优控制系统。三维水平井井眼轨道设计的目的有两个:第一要求动力系统的终端输出与靶点处的对应值越接近越好,第二要求动力系统确定的三维水平井轨道曲线总长最短。基于这两个目的,建立一个不带终端约束的三维水平井井眼轨道最优控制系统;如果在满足给定的入靶精度条件下,只考虑轨道曲线总长最短,那么建立另一个带终端约束的三维水平井井眼轨道设计最优控制系统;由于在三维水平井井眼轨道实际设计中,造斜点处的初值和靶点处的终值的测量是不精确的,给定的一些变量的约束范围也是不精确的,所以在模糊环境下,建立一个三维水平井轨道设计模糊最优控制系统;在实钻过程中,由于客观条件的随机性,三维水平井的实钻轨道往往偏离最优设计的轨道,因此建立一个三维水平井轨道随钻修正设计最优控制系统。并把给出的三种算法应用到三维水平井井眼
    
    大连理工大学博士学位论文
    轨道设计的实际应用中。
     4.目前,所有的对CRS(Controlled Random SearCh)算法的改进工作都是围绕着如
    何产生新的试验点,但是对CRS算法的收敛性工作一直没有考虑。本文基于以一定概率
    接收“可变”单纯形的策略,给出一个改进的CRS算法,证明了改进的算法依概率1收
    敛。
This dissertation, based on practical background of design of 3D-trajectory of horizontal wells, studies nonlinear multistage optimal control systems, including the existence of solutions of state equations and optimal solutions of the optimal control system?optimality conditions?global optimization algorithms for solving the optimal control system and applying these algorithms to practical design of 3D-trajectory of horizontal wells. The main results, obtained in this dissertation, may be summarized as follows:
    1. For a class of nonlinear multistage optimal control systems, the existence of solutions of the state equations and the existence of optimal solutions of the optimal control system and optimality conditions for the optimal control system are discussed.
    2. In order to obtain the global optimal solution of the nonlinear multistage optimal control system, the optimal control system is translated into a nonlinear programming problem. Since the objective function and constraint function can be implicit functions or nondifferentiable in this nonlinear programming problem, three global optimization algorithms, based on stochastic search techniques, are proposed.
    (1) Based on a combination of the uniform design?clustering idea and genetic algorithms, an improved hybrid genetic algorithm is proposed for solving the nonlinear multiage optimal control system without terminal constraints. The convergence of the algorithm is analyzed.
    (2) An improved evolutionary programming algorithm is developed for solving nonlinear multiage optimal control system with terminal constraints. In this algorithm, each individual can be regarded as a charged particle. According to two cases, two kinds of charge are defined on each individual for objective function and each constraint function, respectively. After calculating these charges, like the electromagnetic force a combination force exerted on each individual is calculated. The direction of this force is taken as a search direction of mutation operator. In order to discard infeasible individual with higher probability, a new fitness function is defined. The convergence of this algorithm is proved.
    (3) A global optimization algorithm for solving nonlinear multiage optimal control system with equality terminal constraints is proposed. In this algorithm, firstly, based on aggregate function, a new set that can substitute for the feasible region in which the value of objective function is lower than the current value of objective function is defined, then, in this new set, a feasible point is found by stochastic search and local search. The process stops until the new feasible point cannot be found. The convergence proof of this algorithm is given. According to the idea of the proposed algorithm, a global optimization algorithm is constructed for solving the optimal control system of design of 3D trajectory of horizontal wells with terminal constraints.
    
    
    3. According to the features of 3D-trajectory formed in horizontal wells, we construct a nonlinear multistage dynamical system (i.e. state equations) in which state variables are inclination?azimuth?north coordinate?east coordinate and vertical depth coordinate. The dynamical system describes the 3D-trajectory of horizontal wells. Based on this dynamical system, four optimal control systems of design of 3D-trajectory of horizontal wells are proposed from different points of view. There are two aims for designing 3D-trajectory of horizontal wells. Firstly, the terminal output of this dynamical system will approximate the corresponding values of the target point as close as possible. Secondly, the total length of 3D-trajectory of horizontal wells is as short as possible. Based on the two aims, one optimal control system is developed for designing 3D-trajectory of horizontal wells; if the total length of 3D-trajectory of horizontal wells is only considered under satisfying condition of the given terminal outpu
    t of this dynamical system, then the other optimal control system with terminal constraints is propo
引文
[1]高为炳.非线性控制系统导论.科学出版社,1988.
    [2]孙振纯,许岱文.国内外水平井钻井技术现状初探.石油钻采工艺,1997,19(4):6-12.
    [3]苏义脑,孙宁.我国水平井钻井技术的现状与展望.石油钻采工艺,1996,18(6):14-20.
    [4]Jones H.H.. How to drill a vertical oil well or drilling straight holes by gravity. Oil and Gas Journal, May,1929.
    [5]Clark L.V.W.. A theoretical examinational straight and directed drilling techniques. Journal of Petroleum Techniques, 1931,22(3):87-94.
    [6]Lubinski A.. A study of the buckling of rotary drilling strings. Drilling and Production Practice, 1950,13(1): 178-180.
    [7]Lubinski A.. Factors affecting the angle of inclination and dog-leging in rotary bore holes. Drilling and Production Practice, 1958,37(2): 222-226.
    [8]Murphey C.E.. Hole deviation and drill string behavior. SPE J.,Mar, 1996.
    [9]刘修善等.井眼轨道设计理论与描述方法.黑龙江:黑龙江科学技术出版社,1993.
    [10]Briggs Gary M.. How to design a medium-radius horizontal well. Petroleum Engineer Inte-rnational, 1989,61(9): 1164-1169.
    [11]Solomon S.T., Ross K.C.. Multidissciplined approach to designing targets for horizon-tal wells. Journal of Petroleum Technology, 1994,46(2): 143-149.
    [12]Wiggins Michael L.. Single equation simplifies horizontal directional drilling plans. Oil and Gas Journal, 1992,90(44):74-79.
    [13]冯志明.定向井二次修正的三维设计与计算.钻采工艺,1998,21(3):10-14.
    [14]龚伟安.狗腿严重度问题。石油钻采工艺,1982,4(3):9-17.
    [15]刘福齐.计算井眼实际轨迹的弦步法.天然气工业,1986,6(4):40-46
    [16]杜春常.用三次样条模拟定向井井眼轨迹.石油学报,1988,9(1):112-120.
    [17]刘修善.石在虹.一种测斜计算新方法—自然参数法.石油学报,1998,19(4):87-92.
    [18]刘修善等.计算井眼轨道的曲线结构法.石油学报,1994,15(3):126-133.
    [19]刘修善.石在虹测斜计算理论的新进展,钻采工艺,1998,21(1):7-10
    [20]刘修善等.井眼轨道设计理论与描述方法.黑龙江:黑龙江科学技术出版社,1993
    [21]刘修善,郭均.实际井眼轨迹空间弯曲形态的精确描述.石油钻探技术.1992,20(2):18-20
    [22]Schuh F.J.. Trajectory equations for constant tool face angle deflections. In: Soc of Petroleum Engineers of AIME. Proceedings of Drilling Conference. 1992:111-123.
    [23]Guo Boyun, Lee Robert L., Miska Stefan. Innovation in 3-D drilling trajectory design using concept of constant curvature.In: American Society of Mechanical Engineers (Paper). 1993:1-7.
    [24]Helmy M.W., Khalf F. and Darwish T.A.. Well design using a computer model. SPE Drilling and Completion, 1998,13 (1):42-46.
    [25]Rudolf R.L., McCann R.C., Suryanarayana P.V.R.. Mathematical technique improves directional
    
    well-path planning. Oil and Gas Joumal, 1998,96(34): 64-68.
    [26] 江胜宗,冯恩民.三维水平井井眼轨迹设计最优控制模型及算法.大连理工大学学报,2002,42(3):261-264.
    [27] Wiener R. Cybernetics. New York, 1948.
    [28] Pontryagin et al.The mathematical theory of optimal processes. Interscience Publishers, New York, 1962
    [29] Hartl R F et al. A survey of the maximum principles for optimal control problems with state constraints, SIAM Rev, 1995,37(2):181-218
    [30] Sargent R W H. Necessary condition for optimal control of inequality- constrained DAE systems. Centre for Process Systems Engineering Report. 1998
    [31] Roxin E. The existence of optimal controls. Michigan Math J, 1962,9:109-119.
    [32] Bell M L,Sargent R W H, Vinter R B. Existence of optimal controls for continuous-time infinite horizon problems. Int, J control, 1997,68:887-896.
    [33] Carlson D A, Haurie A. Infinite Horizon Optimal Control. Springer, Berlin, 1987.
    [34] Clark F.H..Nonsmooth analysis and control theory. Springer,1998.
    [35] Loewen P.D.,Rockafeliar R.T.. Optimal control of unbounded differential inclusions. SIAM J. Control and Optimization, 1994,32(2):442-470.
    [36] Loewen P D, Rockafellar R T. New necessary conditions for the generalized problem of Bolze. SIAM J. Control optim., 1996,34:1496-1511.
    [37] Loewen P D, Rockafellar R T. Bolze problems with general time constraints. SIAM J. Control optim., 1997,35:2050-2069.
    [38] Zeiden V. The Riccati equation for optimal control problems with mixed state-control constraints: necessity and sufficiency. SIAM J. Control Optim. 1994,32:1297-1321.
    [39] Bryson A E, Ross S E. Optimum rocket trajectories with aerodynamic drag. Jet propulsion 1958.
    [40] Breakwell J V. The optimization of trajectories. SIAM .1 1959,7:215-247.
    [41] Miele A. Method of particular solutions for linear two-point boundary-value problems. J Optim. Theory Appl. 1968,2(4):315-334.
    [42] Miele A.. Gradient methods in optimal control theory. Optimization and Design, Prentice- Hall, Englewood Cliffs,N J, 1973:323-343.
    [43] Kelley H J. Gradient theory of optimal flight paths. J. Acket Soc. 1960, 30:115-123.
    [44] Breakwell J., Speyer J., Bryson A.E.. Optimization and control of nonlinear systems using the second variation. SIAM J.Control, 1963,1(2):193-223.
    [45] Jacobson D.H., Mayne D.Q.. Differential Dynamic Programming. Elsevier, New York, 1970.
    [46] Lasdon L S, mitter S K. The conjugate gradient methods for optimal control problems. IEEE Trans Automat Control. 1967, 12:132-138.
    [47] Pollard G M, Sargent R W H. Off-line computation of optimum controls for a plate distillation column. Automatica, 1970, 6:59-76.
    
    
    [48] Tsang T H ,et al. optimalcontrol via collocation and nonlinear programming. Int. J. Control, 1975, 21: 763-768.
    [49] Logsdon J S, biegler L T. Accurate solution of differential-algebraic optimization problems. Ind. Eng. Chem. Res., 1989, 28: 1628-1639.
    [50] Tanartkit P, Biegler L T. A nested simultaneous approach for dynamic optimization problems. Comput. Chem. Eng. 1996, 20: 735-741.
    [51] Betts J.T., Huffman W.P.. Application of sparse nonlinear programming to trajectory optimization. J.Guidance Control Dynamics, 1992,15(1): 198-205.
    [52] Dennis j E, Vicente L N. Trust-region interior-point algorithms for a class of nonlinear programming problems. SIAM J. Control Optim. 1998, 36: 1750-1794.
    [53] Morison K R, Sargent r w H. Optimization of multi-stage processes desc- ribed by differential-algebraic equations. Numerical Analysis, springer, berlin, 1986.
    [54] Vassilladis V S, Sargent R W H. Solution of a class of multi-stage dynamic optimization problems:1,problems without path constraints, 2, problems with path constraints. I and EC Res. 1994,33; 2111-2122.
    [57] Bock H G, Plitt K J. A multiple-shooting algorithm for direct solution of optimal control problems. Proceeding of the 9th IFAC World Congress, Budapest, Pergamon Press, Oxford, 1984,1603-1608.
    [56] 刘同仁.用参数最优化方法计算最优飞行.航空学报,1994,15(11): 1298-1305.
    [57] Stoer J, Bulirsch R. Introduction to Numerical Analysis. Springer, New York, 1980.
    [58] Bulirsch R, Montrone F. Abort landing in the presence of a windshear as a minimax optimal control problem: Part 1; necessary condition , Path 2: Multiple shooting and homotopy. Technische Universitat Munchen, Report 1990.
    [59] Maurer H, Gillessen W. Application of multiple shooting to numerical solu- tions of optimal control problems with bounded state variables. Computing 1975, 15:105-126.
    [60] Miele A, Wang T. Parallel computation of two-point boundary-value problems via particular solutions. J. Optim. Theory appl. 1993, 79:5-29.
    [61] Dixon L C W, Bartholomew-Biggs M C. Adjoin-control transformations for solving practical optimal control problems. Optim. Control Appl. Methofs, 1981, 2:365-381.
    [62] Fraser-Andrews G. A multiple-shooting technique for optimal control. J. Optim. Theory Appl., 1999,102:299-313.
    [63] Bell M.L.,Sargent R.W.H.. Optimal control of inequality constrained DAE systems. C0mputers and Chemical Engineering, 2000,24:2385-2404.
    [64] Li x, Yong J. Optimal control theory for infinite dimensional systems. Birkhauser, Boston, 1995.
    [65] 刘康生.非均匀储层储量再估计的适应性。数学物理学报,1996,16:42-47.
    [66] Gensheng Wang. Optimal conyrolof 3-dimentional Navier-Stokes equations with state constraints. SIAM,J. Control Optim.2003,41(2):583-606.
    [67] Wang G, Wang L.. The Carlman inequality and its application to periodic optimal control governed by
    
    semilinear parabolic differential equations. Journal of Optimization Theory and Applications, 2003,118(2):429-461.
    [68] Lou H.. Existence of optimal controls for semilinear parabolic equations elliptic equations. SIAM, J. Control and Optim., 2003,42(1):1-23.
    [69] Lou H.. Maximum principle of optimal control for degenerate quasilinear elliptic without Cesari type constraints.
    [70] 高夯.半线性椭圓方程支配系统的最优性条件.数学学报,2001,44(2):319-332.
    [71] Yao P. E. The observability inequalities of shallow shells. SIAM J. Contr. anti Optim., 2000,38(6): 1729-1756.
    [72] 陈任昭,张丹松,李键全.具有空间扩散的种群系统解的存在唯一性与边界控制.系统科学与数学,2002,22(1):1-13.
    [73] 王康宁.最优控制的数学基础.北京,国防工业出版社,1995.
    [74] Yu W. F.. Identification of distributed parameter with poinwise constraints on the parameters. J. Math. Anal. Appl. 1988,136:497-520.
    [75] Yu W. H. Necessary condition for optimality in the identification of elliptic system with pointwise parameter constraints. J.Optim. Appl. 1996,88:725-742.
    [76] 邓子辰,钟万勰,非线性最优控制系统的时程精细计算研究,计算力学学报,2002,19(2):184-187.
    [77] Trn, A.. A search clustering approach to global optimization. Towards global optimization 2. North-holland, 1978:49-62.
    [78] Rinnooy Kan, A.H.G and Timmer G. Stochastic global optimization methods part Ⅰ: clustering methods. Mathematical Programming, 1987, 39:27-56.
    [79] Rimnooy kan, A.H.G and Timmer, G. Stochastic global optimization methods part Ⅱ: Multi level methods. Mathematical Programming, 1987, 39:57-78.
    [80] Locatelli, M. and Schoen, F.. Random Linkage:a family of acceptance/rejection algorithms for global optimization. Mathematical Programming, 1999,85(2):379-396.
    [81] Price, W.L.. A controlled random search procedure for global optimization. Towards Global Optimization 2,North-Holland, Amsterdam,Holland,1978,71-84.
    [82] Price, W.L.. Global optimization by controlled random search.Journal of Optimization Theory and Applications,1983,40(3):333-348.
    [83] Price, W.L..Global optimization algorithms for a CAD workstation. Journal of Optimization Theory and Applications, 1987,55(1): 133-146.
    [84] Ali,M.M. and Storey, C..Modified controlled random search algorithms. International Journal of Computer Mathematics, 1994,53:229-235.
    [85] Haiton, J.H.. On the efficiency of certain quasi-random sequrnces of points in evaluating multi-dimensional integrals. Numeriche Mathematik, 1960, 2:84-90.
    [86] Ali,M.M.,Trm,A. and Viitanen, S.. A numerical comparison of some modified controlled random search algorithms. Journal of Global Optimization, 1997, 11:377-385.
    
    
    [87] Mohan, C. and Shanker, K.. A mumerical study of some modified versions of controlled random search method for global optimization. International Journal of Computer Mathematics, 1988,23:325-341.
    [88] Ilker Birbil S., and Fang S.C., A multi-point stochastic search method for global optimization. Operation Research and Its Applications, The fourth International Symposium,ISORA'02 Yichang-Chongqing, China, World public- shing Corporation Beijing China, 2002, 1-11.
    [89] Holland,J.H.. Outline for a logical theory of adaptive systems. Journal of the Association for Computing Machinery. 1962,9(3):279-314.
    [90] Fogel, L.J., Owens, A.J. and Walsh, M.J.. Artificial intelligence through simulated evolution. New York, John Wiley & Sons,1966.
    [91] Rechenberg, I.. Evolutionsstrategie: Optimierung technischer systeme nach prinzipien der biologischen evolution. Stuttgart: Frommann-Holzboog Verlag, 1973.
    [92] Bagley, J.D.. The behavior of adaptive systems which employ genetic and correlation algorithms. Doctoral dissertation, University of Michigan, 1967.
    [93] Miller, B. and Goldberg,D.. Genetic algorithms, selection schemes, and the varying effects of noise. Evolutionary Computation, 1996, 4(2):25-49.
    [94] Goldberg, D.E. and Deb, K.. A comparative analysis of selection schemes used in genetic algorithms. In Foundations of Genetic Algorithms, San Mateo,CA: Morgan Kaufmann Publishers, 1991:69-93.
    [95] Baker, J.E.. Adaptive selection methods for genetic algorithms. In Proceedings of the First International Conference on Genetic Algorithms, Hillsdale,NJ: Lawrence Erlbaum Associates J, 1985:101-111.
    [96] Reducting bias and inefficiency In the selection algorithms. In Proceedings of the Second International Conference on Genetic Algorithms, Hillsdale,NJ: Lawrence Erlbaum Associates, 1987:14-21.
    [97] Harik, G. Finding multimodal solutions restricted tournament selection. In Proceedings of the Sixth International Conference on Genetic Algorithms, San Francisco,CA: Morgan Kaufmann, 1995:24-31.
    [98] De Jong, K.A.. An analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation, University of Michigan, 1975.
    [99] Goldberg, D.E.. Genetic algorithms in search, optimization, and machine learning. MA: Addison Wesley,1989.
    [100] Starkweather, T.. A comparison of genetic sequencing operators. In Proceedings of the fourth International Conference on Genetic Algorithms, Los Altos, CA: Morgen Kaufmann Publishers, 1991:69-76.
    [101] Davis, L.. Handbook of genetic algorithm. New York: Van Nostrand Reinhold, 1991.
    [102] Smith, D.. Bin packing with adaptive search.In Proceedings of International Conference on Genetic Algorithms and their applications, 1985:202-206.
    [103] Syswerda, G.. Uniform crossover in genetic algorithms. In: 3rd International Conference on Genetic Algorithms, 1989: 2-9.
    
    
    [104] De Jong, K.A. and Spears, W.M.. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of Mathematics and Artificial Intelligence, 1992,5(1): 1-26.
    [105] Krishnaknmar, K.. Micro-genetic algorithms for stationary and non-stationary function optimization. SPIE Intelligent Control and Adaptive Systems. 1989,1196:289-296.
    [106] Androulakis, I.P. and Venkatasubramanlan, V.. A genetic algorithm framework for process design and optimization. Computer Chem Engng, 1991, 15(4):217-228.
    [107] Grefenstette, J.J.. Parallel adaptive algorithms for function optimization. Technical Report NO.CS-81-19, Nashvilli: Vanderbilt University, Computer Science Department, 1981.
    [108] Stender, J.. Parallel genetic algodthrns: theory and application. Amsterdam: lOS Press, 1993.
    [109] 陈国良,王煦法,庄镇泉.遗传算法及其应用。人民邮电出版社,1996.
    [110] Lin, ET.,Kao,C.Y. and Hsu, C.C.. Applying the genetic approach to Simulated annealing in solving some NP-hard problems. IEEE Trans. SMC, 1993,23 (6): 1752-1767.
    [111] 王凌,郑大钟.一类GASA混合策略及其收敛性研究.控制与决策,1998,13(6):699—672。
    [112] Goldberg, D.E.. Genetic algorithm in search optimization and machine Learning. Reading,MA: Addison Wesley, 1989.
    [113] Hanagandi,Vijay and Michael Nikolaou. A hybrid approach to global optimization using a clustering algorithm in a genetic search framework. Computers Chem Enging, t 998,22(12):1913-1925.
    [114] Goldberg, D.E. and Segrest,P.. Finite Markov chain analysis of genetic algorithm. In Proceedings of the Second International Conference on Genetic Algorithm(ICGA 2), Hillsdale,NJ: Lawrence Erlbaum Associates, 1987:1-8.
    [115] Eiben, A.E.,Arts,E.H. and Van Hee,K.M.. Global convergence of genetic algorithm: an Infinite Markov chain analysis, In Parallel Problem Solving from Nature(PPSN 1). Heidelberg Berlin: Springer-Verlag, 1991: 4-12.
    [116] Rudolph,G. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Network, 1994,5(1):86-101.
    [117] Qi,X.F. and Palmieri, F.. Theoretical analysis of evolution algorithms with an infinite population size in continuous space, Part 1,2: basic properties of selection and mutation. IEEE Transactions on Neural Network, 1994,5(1): 102-129.
    [118] 李书全,寇纪淞.李敏强。遗传算法的随机泛函分析。系统工程学报,1998,13(1):97—101。
    [119] 张文修,徐宗本,聂赞坎,梁怡.遗传算法的概率收敛定理.工程数学学报,2001,18(4):1-11.
    [120] 李敏强,寇纪淞,林丹,李书全.遗传算法的基本理论与应用.北京,科学出版社,2002.
    [121] 孙艳丰,王众托.遗传算法在优化问题中的应用研究进展。控制与决策,1996,11(4):426—431.
    [122] 席裕庚,柴天佑,恽为民.遗传算法综述.控制理论与应用,1996,13(6):697-704.
    [123] 张文修,梁怡.遗传算法的数学基础.西安,西安交通大学出版社,2000.
    [124] Heitkotter, J. and Beasley, D.. The hitch-hiker's guide to evolutionary computation. FAQ in Comp Ai Genetic. 1995.
    
    
    [125] Schwefel, H.P..Numerische optimierung von computer-modellen mittels der evolutions strategic, Interdisciplinary Systems Research, Vol.26,Basel:Birkhuser, 1977.
    [126] Schwefel,H.P.. Numerical optimization of computer models. Chichester, UK: John Wiley, 1981
    [127] Yao, X., and Liu, Y.. Fast evolutionary programming. In Proceedings of the Fifth Annual Conference on Evolutionary Compution, Cambridge, MA: The MIT Press, 1996:441-450.
    [128] Yao, X., and Liu, Y.. Fast evolutionary programming. In Proceedings of the Sixth Annual Conference on Evolutionary Programming, Berlin: Springer-Verlag, 1997:141-161.
    [129] Gehlhaar, D. K. and Fogel, D. B.. Two new mutation operators for enhanced search and optimization in evolutionary programming. In the Proceedings of SPIE, 1997 260-269.
    [130] 林丹,李敏强,寇纪淞.进化规划和进化策略中变异算子的若干研究.天津大学学报,2000,33(5):628—630.
    [131] 郭崇慧,唐焕文.一种改进的进化算法及其收敛性.高等学校计算数学学报,2002,23(1):51—56.
    [132] 郭崇慧,唐焕文.演化策略的全局收敛性.计算数学,2001,23(1):105—110.
    [133] 刘峰,刘贵忠,张茁生.进化规划的Markov过程分析及收敛性.1998,26(8):76-79.
    [134] Kirkpatrick S.,Gelatt C.D., Vecchi M.P..Optimization by simulated annearling. Science, 1983,220:671-680.
    [135] Cardoso M.F.,Salcedo R.L.. Non-equilibrium simulated annealing: a faster approach to combinatorial minimization. Ind. Engng Chem. Res, 1994, 33(8): 1908-1917.
    [136] Eglese R,W.. Simulated annealing, a tool for operational research. European Journal of Operational Research, 1990,46:271-281.
    [137] Vanderbilt D.,Louie S.G.. A monte carlo simulated annealing approach to optimization over continuous variables. Journal of Computational Physics, 1984,56:259-271.
    [138] Patrick Wang P, Der-san Chen.. Continuous optimization by a variant of simulated annealing. Journal of Computational Optimization and Applications, 1996,6:59-71.
    [139] Romeijn H.E., Zabinsky Z.B., Graesser D.L., and Neogi S.. New reflection genertor for simulated annealing in mixed-integer/continuous global optimization. Journal of Optimization Theory and Applications, 1999, 101(2): 403-427.
    [140] 王凌.智能优化算法及其应用.北京,清华大学出版社,2003
    [141] 王卓鹏,高国成,杨卫平.一种改进的快速模拟退火组合优化法.系统工程理论与实践,1999,2: 73—76。
    [142] Dekkers A., Aarts E.. Global optimization and simulated annealing. Mathematical Programming 1991,50:367-393.
    [143] Ali M.M.,Trn A. Viitanen So. A direct search variant of the simulated annealing algoriyhm for optimization involving continuous variables. Computers and Operation Research, 2002,29:87-102.
    [144] 高尚.模拟退火算法中的退火策略研究.航空计算技术,2002,32(4):20-23.
    [145] 杨若黎,顾其发.一种高效的退火全局优化算法.系统工程理论与实践,1997,5:29-35.
    
    
    [146] 庞哈利,郑秉霖,徐心和.一种自适应模拟退火算法.挖控制与决策,1999,14(5):477-480.
    [147] Chen S., Luk B.L.. Adaptive simulated annealing for optimization in signal processing applications. Signal Proccessing, 1999,79:117-128.
    [148] 康立山,谢云,尤矢勇.非数值并行算法-模拟退火算法.北京,科学出版社,1998.
    [149] Cardoso M.E, Salcedo R.L.. The simplex simulated annealing approach to continuous nonlinear optimization. Computers Chem., 1996,20(9): 1065-1080.
    [150] David B.F.. An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks, 1994,5(1):3-12.
    [151] Moilan C.. A controlled random search technique incorporating the simulated annealing concept for solving integer and mixed integer global optimization problems. Computation Optimization and Applications, 1999,14:103-132.
    [152] Belisle C.J.P.. Convergence theorems for a class of simulated annling algorithms on R~d. Journal of Applied Probability, 1992,29:885-895.
    [153] Locatelli M.. Simulated annling algorithms for continuous global optimization: convergence conditions. Journal of Optimization Theory and Applications, 2000, 104(1): 121-133.
    [154] Elijah Polak. Optimization algorithms and consistent approximation. New York, Springer, 1997.
    [155] Davis L.. Bit-climbing, representational bias, and test suitr design . In Proceeding of Fourth Intermational Conference on Genetic Algorithms, San Mateo, CA:Morgan Kaufmann Publishers, 1991: 18-23.
    [156] 方开泰,马长兴.正交与均匀实验设计.科学出版社,北京 2001.
    [157] Hooke R., Jrrvers T. A.. Dirrct search solution of numerical and statistical problems. Computer Math., 1961,8:212-229.
    [158] Hyun Myung and Jong-Hwan Kim. Hybrid evolutionary programming for heavily constrained problems. Bio Systems, 1996,38: 29-43.
    [159] Michalewicz Z.. Genetic algorithms+data structures=evolution programs,3 rd edition. New York: Springer-Verlag, 1996.
    [160] 袁亚湘,孙文瑜.最优化理论与方法.北京,科学出版社,1997.
    [161] Homaifar A., Lai, S., Qi X.. Constraints optimization via genetic algorithms. Simulation, 1994,62(4):242-254.
    [162] Joines J., Houck C. On the use of nonstationary penalty function to solve nonlinear constrained optimization problems with Gas. In Proceedings of the First International Conference on Evolutionary Computation, Piscatawa, NJ: IEEE Press, 1994:569-584
    [163] Michalewicz Z.. Genetic algorithms, numerical optimization, and constraints. In Proceedings of the Sixth International Conference on Genetic Algorithms, San Marco, CA: Morgan Kaufmann Publishers, 1995: 151-158.
    [164] Powell D. and Skolnick M.. Using genetiv algorithms in engineering design optimization with nonlinear constraints. In: Proceedings of the fifth International Conference on Genetic Algorithms,
    
    San Mateo, Morgan Kaufmann Publishers, 1993:424-430.
    [165] 孙艳丰等.基于遗传算法的约束优化方法评述.北方交通大学学报,2000,24(6):14-19.
    [166] 唐加福,汪定伟等.面向非线性规划问题的混合式遗传算法.自动化学报,2000,26(3):401-404.
    [167] 李兴斯.一类不可微优化问题的有效解法.中国科学(A辑),]994,24(4):371-377.
    [168] Ryoo I.I., Sahinidis N.V.. Global optimization of nonconvex NLPs and MINLPs with spplication in process design. Computers and Chemical Engineering, 1995, 5:551-566.
    [169] 下部钻具组合优选方法.见:国家“八五”重大攻关项目,水平井钻井配套技术研究论文集.1993
    [170] Wu H-C. et al. Optimal wellbore planning for deviated wells[A]. Presented at the ASME Energy Sources Technology Conference and Exhibition. Houston. Texas. Jan., 23-26,1994
    [171] 江胜宗,夏尊铨,曹里民。侧钻水平井轨道三维优化设计模型及应用.石油学报,2001,22(3):86-70.
    [172] Lee E.s. and Li R.J., Fuzzy multiple objective programming and compromise programming with pareto optimal, Fuzzy sets and Systems, 1993,53: 275-288.
    [173] Tiwari, R.N, Dharmar, S, and Rao, J.R.. Fuzzy goal programming an additive model. Fuzzy Sets and Systems, 1987, 24, 27-34
    [174] Moiian C., Nguyen H.T.. A controlled random search technique incorporating the simulated annealing concept for solving integer and mixed integer global optimization problems. Computational Optimization and Applications, 1999,14:103-132.
    [175] Ortigosa P.M., Garcia I.. On success rates for controlled random search. Journal of Global Optimization, 2002,21:239-263.
    [176] Ralph D.,Dempe S.. Directional derivatives of the solution of parametric non- linear program. Matheatical Programming, 1995,70:159-172
    [177] Kelly T.K., Kupferschmid M..Numercal verfication of second-order sufficiency conditions for nonlinear programming. SIAM Review, 1998,40(2):310-314.
    [178] 方开泰,田国梁,谢明育.凸多面体上的均匀设计.科学通报,1998,43(14):1472-1475.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700