摘要
作为铅锌火法冶炼的一个重要流程,密闭鼓风烧结过程所生产的烧结块直接影响到后续熔炼炉炉况和铅锌的产量质量指标。烧结生产过程中综合透气性、烧穿点是两个最重要的过程状态参数,直接反映出烧结过程进行的好坏。针对影响综合透气性、烧穿点的信息具有不确定性、不完整性等特点,分别从不同的角度出发进行描述,并建立相应的集成预测模型。
在研究烧穿点和透气性的基础上,提出了烧结块的产量质量预测模型。为使得烧结过程运行在最优状态,将整个烧结过程的优化分为产量质量优化和状态参数优化两大部分。首先提出针对产量质量的优化算法,以获得过程状态参数的优化设定值。接着提出针对过程状态参数的优化算法,以获得过程操作参数的优化设定值。论文的主要研究成果包括:
(1)过程状态参数的集成预测模型
首先考虑到历史数据中含有工况稳态发展趋势的有用信息,结合综合透气性的变化特点,提出具有在线修正能力的灰色理论预测模型,其根据实际综合透气性数据序列的单调性变化情况及变化后的数据个数,给出相应的修正公式;接着考虑到工艺参数预测模型能及时反映工艺参数对工况的影响,建立基于工艺参数的神经网络预测模型;最后考虑到信息熵技术具有降低不确定性带来的影响以及信息融合的能力,建立基于信息熵技术的综合透气性集成预测模型。
针对实际烧结过程烧穿点不能被直接测量的问题,建立烧穿点的软测量模型。考虑到烧穿点的波动比较大,利用T-S模糊模型来建立其预测模型,以充分有效利用历史信息;为降低不确定性带来的影响以及降低建模的难度,将综合透气性指数、台车速度和中部风箱废气温度作为影响烧穿点的主要因素;同样在建立工艺参数的预测模型之后,利用信息熵技术建立烧穿点的集成预测模型。
(2)产量质量智能预测模型
结合机理分析和数据关联性分析,选取过程状态参数以及一些关键的原料参数作为输入变量,并建立烧结块产量质量的神经网络预测模型。针对产量检测周期和过程状态参数的采样周期不一致,存在多尺度问题,引入了空间积分的思想,解决了时间周期不匹配的问题。针对传统神经网络训练算法是基于梯度信息的、具有容易陷入局部极值、收敛速度慢的缺点,将粒子群算法强大的全局搜索能力和共轭梯度法强大的局部搜索能力有机结合起来,提出了一种基于共轭梯度法的混合粒子群算法来进行神经网络的训练。
(3)基于混合粒子群算法的产量质量优化
建立了以提高产量为目标,以质量要求为约束条件的优化模型,并提出了基于改进线性搜索法的混合粒子群算法来实现产量质量的优化。该算法首先将所建立的优化函数转化为一个有两个目标的优化问题,一个是原目标函数,另一个是约束条件违反程度函数;接着为达到并行优化的目的,采用约束水平比较法来比较粒子群算法所搜索到的解;最后当粒子群算法收敛停滞时,通过引入改进的线性搜索法来提高粒子群算法的活性,从而获得综合透气性、烧穿点的优化设定值。
(4)基于多目标粒子群协同算法的过程状态参数优化
建立了以综合透气性、烧穿点等状态参数的优化设定值为目标,以生产边界条件及质量要求为约束条件,以过程操作参数为优化变量的多目标优化模型。针对该模型提出了一种多目标粒子群协同优化算法,采用改进的粒子极值选取法,以及多粒子群合作优化的方式,获得过程操作参数的优化设定值。
最后根据实际工业过程的特点,进行了烧结过程的优化仿真实验。实验结果表明所提出的优化方法能在一定程度上提高烧结块的产量质量。因此所提出的烧结过程优化方法为以后烧结生产的全流程优化提供了技术手段,同时也为复杂工业过程的建模及优化提供一种实用的、值得借鉴的实现方法。
The sintering agglomerate of the imperial updraft sintering process, which acts as an important flow process of Lead-Zinc pyrometallurgical smelter processes, has a direct influence on the subsequent states of the smelting furnace and the indexes of quantity and quality of Lead-Zinc. The synthetic permeability and the burn-through-point (BTP) are the two most important parameters of process states, and could directly reflect the performance of the sintering process. Considering that the information influencing the synthetic permeability and the burn-through-point (BTP) features uncertainties and incompletion, the thesis describes them from different aspects respectively, in order to build the corresponding integration prediction models for the synthetic permeability and the BTP.
The prediction models, which are based on the study of the BTP and the synthetic permeability, for the quantity and the quality of the sintering agglomerate are proposed. To obtain good performance of sintering process with optimal states, the optimization of sintering process is divided into two parts. The one is the optimization of quantity and quality, and the other is the optimization of process states. First, to obtain the optimal set values of the process states, the optimization algorithm for the quantity and quality is devised. Then, to obtain the optimal set values of the operation parameters of the sintering process, the optimization algorithm for the parameters of process states is presented. The main achievements are as follows:
(1) Integrated prediction models for parameters of the process states
First, the prediction model for the synthetic permeability based on the gray theory with the capability of modification on line is proposed, since the history data preserves the effective information that reflects the operation trend of future steady states. The corresponding formulas are proposed according to the monotonic changes of the datum sequence of the synthetic permeability and the data numbers after these changes. Then, the technological-parameter-based prediction model using neural networks for the synthetic permeability is established, which can timely reflect the influence brought by the technological parameters. Finally, the information entropy technology is applied to establish the integrated prediction model for the synthetic permeability, due to its abilities of decreasing the uncertainties and fusing the information.
The soft measurement model for the BTP is developed, to solve the problem that the BTP can't be measured directly: Considering that the fluctuation of BTP is relatively big, the T-S prediction model for the BTP is presented to make full use of the history information. To decrease the effects produced by the uncertainties and lower the difficulty of modeling, the synthetic permeability, the strand velocity and the gas temperature of middle bellows are selected as the main factors impacting the BTP. Similarly, the prediction model based on neural networks is present. And the information entropy technology is applied to integrate the prediction models mentioned above.
(2) Intelligent prediction models for the quantity and quality of the Lead-Zinc sintering agglomerate
The parameters of process states and some key parameters of raw material are chosen to build the prediction models for the quantity and quality of the sintering agglomerate using neural networks. The problem of the different time scales between the measurement period of quantity of the product and the sample period of parameters of process states is solved using the spatial integral method. To avoid the slow convergence and easily falling into the local optima of BP training algorithm based on gradient information, a hybrid particle swarm optimization (PSO) is employed to train the neural networks, which integrates the global search ability of the PSO and the powerful local search ability of the conjugate gradient algorithm.
(3) Optimization of quantity and quality of the sintering agglomerate based on hybrid PSO algorithm
An optimization model with the constraint conditions of quality requirements is established to enhance quantity, and a hybrid PSO algorithm based on the improved line searching algorithm is presented to realize the optimization of quantity and quality. First, the optimization model is converted to a two-objective optimization problem, one of them is the origin objective function, and the other is the degree function of constraint violation. Then, to realize the parallel optimization, a constraint comparison method is applied to compare the searched results of the PSO. Finally, to handle the problem of premature convergence frequently appeared in the PSO, an improved line searching algorithm is introduced to maintain the particle activation when PSO stagnates. So the optimal set values of the synthetic permeability and the burn-through point could be achieved by using the hybrid PSO algorithm.
(4) Optimization of parameters of the process states based on multi-objective particle swarm cooperative optimization algorithm
With the optimal set values of the synthetic permeability and the BTP as the optimization targets, the production frontier and quality requirements as the constraint conditions, and the parameters of process states as the optimization valuables, a multi-objective optimization model is developed. It is optimized by a multi-objective particle swarm cooperative optimization algorithm, which is improved using a new principle of selecting the particles' optima, and multiple swarms optimizing in a cooperative way, so that the optimal set values of process operation are obtained.
Finally, the simulation experiments of the optimization of sintering process are implemented according to the characteristics of the practical production process. The results of experiments show the presented optimization method in the thesis improved the quantity and quality of the sintering agglomerate to some extent. So the presented optimization method provides technical means for the optimization of the whole sintering production process, and yields a practical and effective method for modeling and optimization of the complex industrial process.
引文
[1]唐贤容,王笃阳,张清岑.烧结理论与工艺[M].长沙:中南工业大学出版社,1992.
[2]周取定,孔令坛.铁矿石造块理论及工艺[M].北京:冶金工业出版社,1989.
[3]Siemon J.R.,Kowalczyk E.,Fitzgibbons D.P.,Baguley W..Peak bed temperature prediction on a lead/zinc sinter plant[J].Minerals Engineering,1991,4(1):63-78.
[4]Cheng W.S..An application of adaptive genetic-neural algorithm to sinter's BTP process[C].Proceedings of 2004 International Conference on Machine Learning and Cybernetics,Shanghai,2004,6:3356-3360.
[5]吴祈宗.运筹学与最优化方法[M].北京:机械工业出版社,2003.
[6]邱菀华,冯允成,魏法杰,周泓.运筹学教程[M].北京:机械工业出版社,2004.
[7]Yeniay 0..Penalty function methods for constrained optimization with genetic algorithms.Mathematical and Computational Applications,2005,10(1):45-56.
[8]Ho P.Y.,Shimizu K..Evolutionary constrained optimization using an addition of ranking method and a percentage-based tolerance value adjustment scheme [J].Information Sciences,2007,177(14):2985-3004.
[9]Iruthayarajan M.W.,Baskar S.Evolutionary algorithms based design of multivariable PID controller[J].Expert Systems with Applications,2009,36(5):9159-9167.
[10]Zhang Z.H..Immune optimization algorithm for constrained nonlinear multiobjective optimization problems[J].Applied Soft Computing,2007,7(3):840-857.
[11]Y(?)d(?)z A.R..A novel hybrid immune algorithm for global optimization in design and manufacturing[J].Robotics and Computer-Integrated Manufacturing,200925(2):261-270.
[12]Coello C.A.C..Theoretical and numerical constraint-handling techniques used with evolutionary algorithms:a survey of the state of the art[J].Computer Methods in Applied Mechanics and Engineering,2002,191(11-12):1245-1287.
[13]Medaglia Andr(?)s L.,Graves Samuel B.,Ringuest Jeffrey L..A multiobjective evolutionary approach for linearly constrained project selection under uncertainty[J].European Journal of Operational Research,2007,179(3):869-894.
[14]Cabrera J.A.,Nadal F.,Mu(?)oz J.P.,Simon A..Multiobjective constrained optimal synthesis of planar mechanisms using a new evolutionary algorithm[J].Mechanism and Machine Theory,2007,42(7):791-806.
[15]Cumming M.J.,Rankin W.J.,Siemaon J.R..Modeling and Simulation of Iron Ore Sintering[C].In 4~(th) International Symposium on Agglomeration,Canada,1985:763-766.
[16]Upadhyaya G.S..Sintering 2000:a report[J].Materials and design,2001,22(3):239-242.
[17]Hanko G.,Lebleu A.,Siboby M..Optimization of the New Jersey refining process.In:Dutrizac J.E.,Gonzalez J.A.,Henke D.M..Lead-Zinc 2000.Pittsburgh:TMS,2000.481-496.
[18]Tamure N.,Konishi M..Mathematical approach for the optimization of sintering process Operation.In:Yoshitani Y..Proceedings of the IFAC MMM symposium on automation in mining,mineral and metal processing,Pergamon Press:Oxford,1998:203-208.
[19]Patission F..Study of moister transfer during the strand sintering process[J].Metal Transactions,1990,2(21B):37-47.
[20]王文忠,刘百臣,杜钢.脉动气流对烧结过程的强化作用[J].包头钢铁学院学报,1999,18(3):171-173.
[21]胡长庆,张玉柱.烧结过程中的传输理论[J].烧结球团,2000,25(3) 1-4.
[22]袁熙志,周取定.铁矿石烧结过程基本理论的研究.见:徐瑞图,吴胜利,编.中国铁矿石烧结研究-周取定教授论文集.北京:冶金工业出版社,1997 203-211
[23]Gupta S.S.;Venkataramana R..Mathematical model of air flow during iron ore sintering process[J].Iron and Steelmaker,2000,27(12):5-41.
[24]Upadhyaya G.S..Some issues in sintering science and technology[J].Materials chemistry and physics,2001,67(1):1-5.
[25]Hamada K.,Matoba Y.,Marai T..Systeme de controle de la composition chimique de I' agglomere[J].Revue de Metallurgic-CIT,1987,(5):409-419.
[26]Er M.J.,Liao J.,Lin J.Y..Fuzzy-Neural-Network-Based Quality Prediction System for Sintering Process[J].IEEE Transaction on fuzzy system,2000,8(3):314-324.
[27]范晓慧;曾垂喜;姜涛;陈许玲;龙红明;胡友明;张克诚.铁矿石烧结性能预报模型[J].中南大学学报(自然科学版),2005,36(6):949-954.
[28]蒋大军,唐炜.利用狄色系统建模预测烧结生产指标及其影响因素分析[J].烧结球团,1998,23(5):27-31.
[29]陈晓方,桂卫华,王雅琳.基于智能集成策略的烧结块残硫软测量模型[J].控制理论与应用,2004,21(1):75-80.
[30]万维汉,万百五,史维祥,杨金义.闪速炉的仿人模糊质量控制模型[J].西 安交通大学学报,2001,35(7):700-704.
[31]赵德宽,王京,王海,张文宇.基于专家系统的烧结生产质量监督和操作指导控制系统[J].冶金自动化,2004,28(2):26-29.
[32]王雅琳,桂卫华,阳春华,陈晓方,张传福.基于综合建模方法的铅锌烧结块成分预测[J].中南大学学报,2005,36(1):113-118.
[33]陈晓方,桂卫华,蔡自兴,吴敏.过程控制中的智能集成建模方法[J].系统仿真学报,2001,13(1):8-11.
[34]常亮,王介生.以透气性为中心的链篦机过程智能优化控制[J].鞍山科技大学学报,2006,29(6):599-602.
[35]唐朝晖,桂卫华,吴敏,陈晓方.基于神经网络和灰色理论的密闭鼓风炉透气性预测模型[J].中国有色金属学报,2003,13(5):1306-1310.
[36]周永孝,桂卫华,周先德,赵智国.基于模糊算法的炉料粒级分布优化系统[J].计算技术与自动化,2001,20(3):37-40,52.
[37]陈向贵.铅锌烧结过程透气性的智能集成建模与优化控制[J].昆明冶金高等专科学校学报,23(1):5-11.
[38]陈伟,高现刚,王艳,关红兵.烧结返矿提前润湿工艺的应用[J].烧结球团,2005,30(5):32-34.
[39]#12
[40]Corson R.C.,邓庆球.烧结厂控制的新发展[J].烧结球团,1984,9(5):97-108
[41]Cheng W.S..Prediction system of burning through point(BTP) based on adaptive pattern clustering and feature map[C].Proceedings of 2005international conference on machine learning and cybernetics,Dalian,2005,3089-3094.
[42]Li,M.H.Wang,J..The research for soft measuring technique of sintering buming through point[C].1~(ST) IEEE conference on industrial electronics and applications,2006:1-4.
[43]Wu X.F.,Fei M.R.,Wang H.S.,Zheng S.B..Prediction of sinter bum-through point based on support vector machines[C].Intelligent Control and Automation.International Conference on Intelligent Computing,2006:722-730.
[44]Li P.,Ji Z.C.,Tan J.D..Sintering finish point intelligent control[C].Proceedings of the 2005 IEEE/ASME international conference on advanced intelligent mechatronics monterey,California,2005:24-28.
[45]李桃,冯其明,范晓慧,姜涛.基于自组织神经网络的烧穿点自适应预报系统的开发[J].计算机工程与应用,2001,37(6):127-129.
[46]王亦文,桂卫华,王雅琳.基于最优组合算法的烧穿点集成预测模型[J].中国有色金属学报,2002,12(1):191-195.
[47]刘玉长,桂卫华,周孑民.基于软测量技术的模糊烧穿点控制研究[J].烧结球团,2002,27(2):27-30.
[48]李明河,王群京;孙雁飞.基于神经网络的烧穿点预报的研究[J].合肥工业大学学报,2004,27(6):631-634.
[49]张苹;韩大平;郝晓静;杜钢.BP算法的模糊神经网络及烧穿点辅助预测模型[J].材料与冶金学报,2004,3(2):157-160.
[50]Homaifar A.,Lai S.,Qi X..Constrained optimization via genetic algorithms[J].Simulation,1994,62(4):242-254.
[51]Joines J.A.,Houch C.R..On the use of non-stationary penalty functions to solve nonlinearconstrained optimization problems with GA's[C].Process of the First IEEE Conference on Evolutionary Computation,Orlando,1994:579-584.
[52]Hamida S.B.,Schoenauer M..Aschea:new results using adaptive segregational constraint handling[C].Proceedings of the Congress on Evolutionary Computation 2002(CEC 2002),Honolulu,2002:884-889.
[53]Wright J.A.,Farmani R..Genetic algorithm:a fitness formulation for constrained minization[C].Proceedings" of Genetic and Evolutionary Computation Conference,San Francisco,2001:725-732.
[54]Farmani R.,Wright J.A..Self-adaptive fitness formulation for constrained optimization[J].IEEE Transactions on Evolutionary Computation,2003,7(5):445-455.
[55]Runarsson T.P.,Yao X..Stochastic ranking for constrained evolutionary optimization[J].IEEE Transactions on Evolutionary Computation,2000,4(3):284-294.
[56]Yu J.X.,Yao X.,Choi C.,Gou G..Materialied view selection as constrained evolutionary optimization[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C:Applications and Reviews,2003,33(4):458-467.
[57]Powell D.,Skolnick M.M..Using genetic algorithm in engineering design optimization with nonlinear constraint[C].Proceedings of the 5th International Conference on Genetic Algorithms,San Mateo,1993:424-431.
[58]Deb K..An efficient constraint handling method for genetic algorithms[J].Computer Methods in Applied Mechanics and Engineering,2000,186(2-4):311-338.
[59]Montes M.E.,Coello C.A.C..A simple multimembered evolution strategy to solve constrained optimization problems[J].IEEE Transactions on Evolutionary Computation,2005,9(1):1-17.
[60]林丹,李敏强,寇纪淞.基于遗传算法求解约束优化问题的一种算法[J].软件学报,2001,12(4):628-632.
[61]Runarsson T.P.,Yao X..Search Biases in Constrained Evolutionary Optimization[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C:Applications and Reviews,2005,35(2):233-243.
[62]Takahama T.,Sakai S..Constrained optimization by applying the a constrained method to the nonlinear simplex method with mutations[J].IEEE Transactions on Evolutionary Computation,2005,9(5):437-451.
[63]周育人,李元香,王勇,康立山.Pareto强度值演化算法求解约束优化问题[J].软件学报,2003,14(7):1243-1249.
[64]Surry PD,Radcliffe NJ..The comoga method:constrained optimization by multiobjective genetic algorithm[J].Control and Cybernetics,1997,26(3):391-412.
[65]Coello C.A.C..Treating constraints as objectives for single-objective evolutionary optimization[J].Engineering Optimization,2000,32(3):275-308.
[66]Fonseca C.M.,Fleming P.J..Multiobjective optimization and multiple constraint handling with evolutionary algorithms-part Ⅰ:a unified formulation [J].IEEE Transactions on Systems,Man,and Cybernetics,Part A:Systems and Humans,1998,28(1):26-37.
[67]Coello C.A.C..Constraint-handling using an evolutionary multiobjective optimization technique[J].Civil engineering and environmental systems,2000,17(4):319-346.
[68]Coello C.A.C.,Montes M.E..Constraint-handling in genetic algorithms through the use of dominance-based tournament Selection[J].Advanced Engineering Informatics,2002,16(3):193-203.
[69]Horn J.,Nafpliotis N.,Goldberg D.E..A niched Pareto genetic algorithm for multiobjective optimization[C].Proceedings of the First IEEE Conference on Evolutionary Computation,Orlando,1994:82-87.
[70]Ray T.,Liew K.M..Society and civilization:an optimization algorithm based on the simulation of social behavior[J].IEEE Transactions on Evolutionary Computation,2003,7(4):386-396.
[71]Venkatraman S.,Yen G.G..A generic framework for constrained optimization using genetic algorithms[J].IEEE Transaction on Evolutionary Computaion,2005,9(4):424-435.
[72]王勇,蔡自兴,曾威,刘慧.求解约束优化问题的一种新的进化算法[J].中南大学学报,2006,37(1):119-123.
[73]谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,18(2):129-134.
[74]Kennedy J.,Eberhart R.C..Particle swarm optimization[C].Proceedings of the IEEE International Conference on Neural Networks,Perth,1995:1942-1948.
[75]Eberhart R.C.,Kennedy J..A new optimizer using particles swarm theory[C]. Proceedings of the Sixth International Symposium on Micro machine and Human Science,Nagoya,1995:39-43.
[76]Shi Y.,Eberhart R..A modified particle swarm optimizer[C].Proceedings of the IEEE 1998 Congress on Evolutionary Computation,Anchorage,1998:69-73.
[77]Parsopoulos K.E.,Plagianakos V.P.,Magoulas G.D.,Vrahatis M.N..Improving particle swarm optimizer by function "stretching"[C].Advances in Convex Analysis and Global Optimization,Netherlands,2001:445-457.
[78]Shi Y.,Eberhart R..Fuzzy adaptive particle swarm optimization[C].Proceedings of the 2001 Congress on Evolutionary Computation,Seoul,2001:101-106.
[79]Clerc M..The swarm and the queen:towards a deterministic and adaptive particle swarm optimization[C].Proceedings of the 1999 Congress on Evolutionary Computation,Washington,1999:1951-1957.
[80]Angeline P.J..Using selection to improve particle swarm optimization[C].Proceedings of the IEEE International Conference on Evolutionary Computation,Anchorage,1999:84-89.
[81]Lovbjerg M.,Rasmussen T.K.,Krink T..Hybrid particle swarm optimizer with breeding and subpopulations[C].Proceedings of the 3~(rd) Genetic and Evolutionary Computation Conference,San Francisco,2001.
[82]付国江,王少梅,刘舒燕,李宁.含边界变异的粒子群算法[J].武汉理工大学学报,2005,27(9):101-103,114.
[83]王俊伟,汪定伟.一种带有梯度加速的粒子群算法[J].控制与决策,2004,19(11):1298-1300.
[84]杨维,李歧强.粒子群优化算法综述[J1.中国工程科学,2004,6(5):87-94.
[85]He Q.,Wang L..A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization[J].Applied Mathematics and Computation,2007,2(186):1407-1422.
[86]Luo Y.Q.,Yuan X.G.,Liu Y.J..An improved PSO algorithm for solving nonconvex NLP/MINLP problems with equality constraints[J].Computers &Chemical Engineering,2007,31(3):153-162.
[87]Zhang H.,Li H.,Tam C.M..Particle swarm optimization for resource-constrained project scheduling[J].International Journal of Project Management,2006,24(1):83-92.
[88]Akjiratikarl C.,Yenradee P.,Drake P.R..PSO-based algorithm for home care worker scheduling in the UK[J].Computers & Industrial Engineering,2007,53(4):559-583.
[89]Cai J.J.,Ma X.Q.,Li L.X.,Peng H.P..Chaotic particle swarm optimization for economic dispatch considering the generator constraints[J].Energy Conversion and Management,2007,48(2):645-653.
[90]Coello C.A.C..A comprehensive survey of evolutionary-based multiobjective optimization techniques[J].Knowledge and Information Systems,1999,3(1):269-308.
[91]Rosenberg R.S..Simulation of genetic populations with biochemical properties [D].Ann Harbor,Michigan:University of Michigan,1967.
[92]Schaffer J.D..Multiple objective optimization with vector evaluated genetic algorithms[C].Proceedings of the First International Conference on Genetic Algorithms,Lawrence Erlbaum,1985:93-100.
[93]Coello C.A.C..Twenty years of evolutionary multi-objective optimization:a historical view of the field[J].IEEE Computational Intelligence Magazine,2006,1(1):28-36.
[94]Fonseca C.M.,Fleming P.J..Genetic algorithms for multi-objective optimization:formulation,discussion and generalization[C].Proceedings of the Fifth International Conference on Genetic Algorithms,San Mateo,California,1993:416-423.
[95]Srinivas N.,Deb K..Multiobjective optimization using nondominated sorting in genetic algorithms[J].Evolutionary Computation,1994,2(3):221-248.
[96]Horn J.,Nafpliotis N.,Goldberg D.E..A niched pareto genetic algorithm for multiobjective optimization[C].Proceedings of the First IEEE Conference on Evolutionary Computation,New Jersey,1994:82-87.
[97]Zitzler E.,Thiele L..Multiobjective evolutionary algorithms:a comparative case study and the strength pareto approach[J].IEEE Transactions on Evolutionary Computation,1999,3(4):257-271.
[98]Zitzler E.,Laumanns M.,Thiele L..SPEA2:Improving the strength pareto evolutionary algorithm[C].Evolutionary Methods for Design,Optimization and Control with Applications to Industrial Problems,Greece,2002:95-100.
[99]Knowles J.D.,Come D.W..Approximating the nondominated front using the pareto archived evolution strategy[J].Evolutionary Computation,2000,8(2):149-172.
[100]Deb K.,Agrawal S.,Pratab A.,Meriyavan T..A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization:NSGA-Ⅱ.Proceedings of the Parallel Problem Solving from Nature VI Conference,Paris,2000:849-858.
[101]Erickson M.,Mayer A.,Horn J..The niched pareto genetic algorithm 2 applied to the design of groundwater remediation systems[M].Evolutionary Multi-Criterion Optimization,2001:681-695.
[102]Corne D.W.,Knowles J.D.,Oates M.J..The pareto envelope based selection algorithm for multiobjective optimization[C].Proceedings of the Parallel Problem Soving from Nature VI Conference,Paris,2000:839-48.
[103]郑向伟,刘弘.多目标进化算法研究进展[J].计算机科学,2007,34(7):187-192.
[104]王俊年;刘建勋;陈湘州.一种多目标微粒群算法及其收敛性分析[J].计算机工程与应用,2007,43(22):53-55,92.
[105]孙小强,张求明.一种基于粒子群优化的多目标优化算法[J].计算机工程与应用,2006,42(18):40-42,78.
[106]张利彪,周春光,马铭,刘小华.基于粒子群算法求解多目标优化问题[J].计算机研究与发展,2004,41(7):1286-1291.
[107]Coello C.A.C.,Pulido G.T.,Lechuga M.S..Handling multiple objectives with particle swarm optimization[J].IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.
[108]Parsopoulos K.E.,Vrahatis M.N..Particle swarm optimization method in multiobjective problems[C].Proceedings of the 2002 ACM symposium on Applied computing,New York,2002:603-607.
[109]Hu X.H.,Eberhart R..Multiobjective optimization using dynamic neighborhood particle swarm optimization[C].IEEE Congress on Evolutionary Computation,Hawaii,2002:1677-1681.
[110]熊盛武,刘麟,王琼,史明.改进的多目标粒子群算法[J].武汉大学学报,2005,51(3):308-312.
[111]王丽,刘玉树,徐远清.基于在线归档技术的多目标粒子群算法[J].北京理工大学学报,2006,26(10):883-887.
[112]金欣磊,马龙华,刘波,钱积新基于动态交换策略的快速多目标粒子群优化算法研究[J].电路与系统学报,2007,12(2):78-83.
[113]申晓宁,郭毓,陈庆伟,胡维礼.一种子群体个数动态变化的多目标优化协同进化算法[J].控制与决策,2007,22(9):1011-1016.
[114]《铅锌冶金学》编委会.《铅锌冶金学》[M].北京:科学出版社,2003.
[115]邓聚龙.灰理论基础[M].武汉,华中科技大学出版社,2002.
[116]Thananchai L..Grey prediction on indoor comfort temperature for HVAC systems[J].Expert Systems with Applications,2008,34(4):2284-2289.
[117]Wang C.H.,Hsu L.C..Using genetic algorithms grey theory to forecast high technology industrial output[J].Applied Mathematics and Computation,2008,195(1):256-263.
[118]Lo S.P.,Chang D.Y.,Lin Y.Y..Quality prediction model of the sheet blanking process for thin phosphorous bronze[J].Journal of Materials Processing Technology,2007,194(1-3):126-133.
[119]熊崇俊,宁宣熙,潘颖莉.运用熵的组合预测法对我国客运总量的预测[J]. 统计与决策,2006,(6):66-68.
[120]张培学,姚慧,郑新奇.熵权组合模型在人口预测中的应用[J].水土保持研究,2007,14(4):103-104.
[121]W.Richard Scoa.组织理论:理性、自然和开放系统[M].北京:华夏出版社,2002.
[122]杜玉晓,吴敏,岑丽辉,桂卫华.铅锌烧结过程的集成建模方法及智能优化算法[J].小型微型计算机系统,2004,25(8):1458-1463.
[123]Zhou Z.L.,Li M.Z..Mathmatical statistics[M].Beijing:China Statistics press,1987.
[124]Hagan M.T.,Demuth H.B.,Beale M.H..Neural network design[M].Beijing:China Machine Press,2002.
[125]Cheng W.S.,Fei M..A building of the genetic-neural network for sinter's burning through point[C].Proceedings of the International Conference on Machine Learning and Cybernetics,Shanghai,2004,6:3356-3360.
[126]Feng Q.M,Li T.,Fan X.H.,Jiang T..Adaptive prediction system of sintering through point based on self-organizing artificial neural network[J].Transactions of the Nonferrous Metals Society of China,2000,10(6):804-807.
[127]李少远,李柠.复杂系统的模糊预测控制及其应用[M].北京:科学出版社,2003:16-18.
[128]李柠,李少远,席裕庚.利用模糊满意聚类建立pH中和过程模型[J].控制与决策,2002,17(2):143-147.
[129]Takagi T.,Sugeno M..Fuzzy identification of systems and its applications to modeling and control[J].IEEE Transactions on Systems,Man,and Cybernetics,1985,15(1):116-132.
[130]曹贺,董金波.机电系统Kalman滤波参数辨识方法研究[J].煤炭技术,2007,26(7):23-25.
[131]高媛,毛琳,梁佐江,邓自立.两传感器按对角阵加权信息融合稳态Kalman 滤波器[J].黑龙江大学自然科学学报,2004,21(2):52-54.
[132]仲卫进,艾芊.扩展卡尔曼滤波在动态负荷参数辨识中应用[J].电力自动化设备,2007,27(2):47-50.
[133]潘美芹,贺国强.基于共轭梯度法的函数优化混合遗传算法[J].山东科技大学学报,2000,19(4):10-13.
[134]吴祈宗.运筹学与最优化方法[M].北京:机械工业出版社,2003.
[135]李旭渊,许化龙.一种基于免疫小生境思想的粒子群优化算法.计算机工程与应用,2008,44(8):95-97.
[136]Trelea I.C..The particle swarm optimization algorithm:convergence analysis and parameter selection[J].Information Processing Letters,2003,85(6):317-325.
[137]Eberhart R.C.,Shi Y..Comparing inertia weights and constriction factors in particle swarm optimization[C].Proceedings of the 2000 Congress on Evolutionary Computation,La Jolla,2000:84-88.
[138]Van den Bergh F.An analysis of particle swarm optimizers[D].South Africa:University of Pretoria,2002.
[139]Camponogara E.,Talukdar S.N..A genetic algorithm for constrained and multiobjective optimization[C].In 3rd Nordic Workshop on Genetic Algorithm and Their Application(3NWGA),Finland,1997:49-62.
[140]韩江洪,李正荣,魏振春.一种自适应粒子群优化算法及其仿真研究系统[J].仿真学报,2006,18(10):2969-2971.
[141]金欣磊,马龙华,吴铁军,钱积新.基于随机过程的PSO收敛性分析[J].自动化学报,2007,33(12):1263-1268.
[142]Clerc M.,Kennedy J..The particle swarm-explosion,stability,and convergence in a multidimensional complex space[J].IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
[143]张丽平.粒子群优化算法的理论及实践[D].2005.
[144]Himmelblau D.M..Applied nonlinear programming[M].New york:McGraw-Hill,1972.
[145]Homaifar A.,Lai S.H.V.,Qi X..Constrained optimization via genetic algorithms[J].Simulation,1994,62(4):242-254.
[146]Ming Y.C.,Kim J.H.,Jo J..A population ecology inspired parent selection strategy for numerical constrained optimization problems[J].Applied mathematics and Computation,2007,190(1):292-304.
[147]Michalewicz Z..Genetic algorithms,numerical optimization,and constraints [C].Proceedings of the sixth International Conference on Genetic Algorithms,San Mateo,1995:151-158.
[148]Deb K.,Pratap A.,Agarwa S.,Meyarivan T..A fast and elitist multiobjective genetic algorithm:NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[149]Coello C.A.C.,Lechuga M.S..MOPSO:a proposal for multiple objective particle swarm optimization[C].Proceedings of the 2002 Congress on Evolutionary Computation,New Jersey,2002:1051-1056.