集装箱龙门起重机结构系统多目标动态优化研究
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摘要
由于多目标优化技术在工程、经济、管理和军事等领域中具有重要的应用价值,多目标优化的研究越来越受到广泛的关注和重视,它已发展成为一门新兴的学科并在应用中显示出强大的生命力。遗传算法是借鉴生物的自然选择和遗传机制而开发出的一种全局优化自适应概率搜索算法,它在解决复杂系统优化时所表现出的独特的优越性和健壮性,使其成为解决多目标优化问题的一个非常有效的手段。
由于龙门起重机结构系统的动态特性很难用设计变量精确显式表达,本文利用多层人工神经网络极强的非线性映射功能,来描述和处理动态系统中设计变量及其动态参数之间的关系。人工神经网络模型一旦建立,可取代有限元模型进行结构动态特性重分析,其分析过程简单而直接,且远比有限元模型计算速度快,尤其适用于工程技术人员使用。因此,利用遗传算法对所建立的神经网络模型寻优,可以得到可行区域内动态特性最优时的设计变量及目标值。
本文在有限元分析的基础上,结合正交试验法,利用BP神经网络建立了振动系统快速重分析的数学模型,并利用遗传算法对神经网络模型寻优,最终得到多目标优化的部分Pareto最优解。
In view of the importance of multi-objective optimization in engineering, economy, management, military and so on, the research on multi-objective optimization has been paid more attention. It has developed into a new branch of science and demonstrated powerful vitality in application. The genetic algorithm is a global optimization, auto-adapted, probability-based searching algorithm which uses the experience of biological natural selection and genetic mechanism for reference. Owing to its unique superiority and robustness in solving the complex system optimization, it becomes a very effective method in solving multi-objective optimization problems.
The dynamic behaviour of gantry container crane structure system is very difficult to be explicitly expressed with design variables. As a result of its powerful nonlinear mapping ability, multi-layer neural networks are used to describe and deal with the relations between design variables and dynamic parameters of the structure system. Once the neural networks model is built, it can substitute the finite element model, and be used for the reanalysis of structure dynamic behaviour. The analysis process is simple and direct. Moreover, computation speed of the neural networks model is faster than the finite element model, which applies to engineers and technicians, especially. Therefore, genetic algorithm is used to optimum the built neural networks model, to get the design variables and target values in the feasible zone when the dynamic behaviour is optimal.
In this paper, BP neural networks are employed in combination with finite element analysis and orthogonal experiment method, to build the mathematical models of the vibration system for a rapid re-analysis. And genetic algorithm is used to optimum the neural networks model. Eventually, we get part of the Pareto optimal solutions.
由于龙门起重机结构系统的动态特性很难用设计变量精确显式表达,本文利用多层人工神经网络极强的非线性映射功能,来描述和处理动态系统中设计变量及其动态参数之间的关系。人工神经网络模型一旦建立,可取代有限元模型进行结构动态特性重分析,其分析过程简单而直接,且远比有限元模型计算速度快,尤其适用于工程技术人员使用。因此,利用遗传算法对所建立的神经网络模型寻优,可以得到可行区域内动态特性最优时的设计变量及目标值。
本文在有限元分析的基础上,结合正交试验法,利用BP神经网络建立了振动系统快速重分析的数学模型,并利用遗传算法对神经网络模型寻优,最终得到多目标优化的部分Pareto最优解。
In view of the importance of multi-objective optimization in engineering, economy, management, military and so on, the research on multi-objective optimization has been paid more attention. It has developed into a new branch of science and demonstrated powerful vitality in application. The genetic algorithm is a global optimization, auto-adapted, probability-based searching algorithm which uses the experience of biological natural selection and genetic mechanism for reference. Owing to its unique superiority and robustness in solving the complex system optimization, it becomes a very effective method in solving multi-objective optimization problems.
The dynamic behaviour of gantry container crane structure system is very difficult to be explicitly expressed with design variables. As a result of its powerful nonlinear mapping ability, multi-layer neural networks are used to describe and deal with the relations between design variables and dynamic parameters of the structure system. Once the neural networks model is built, it can substitute the finite element model, and be used for the reanalysis of structure dynamic behaviour. The analysis process is simple and direct. Moreover, computation speed of the neural networks model is faster than the finite element model, which applies to engineers and technicians, especially. Therefore, genetic algorithm is used to optimum the built neural networks model, to get the design variables and target values in the feasible zone when the dynamic behaviour is optimal.
In this paper, BP neural networks are employed in combination with finite element analysis and orthogonal experiment method, to build the mathematical models of the vibration system for a rapid re-analysis. And genetic algorithm is used to optimum the neural networks model. Eventually, we get part of the Pareto optimal solutions.
引文
[1]张质文,王金诺.起重机设计手册[M].北京:中国铁道出版社,1998.
[2]黄大巍,李风,毛文杰.现代起重运输机械[M].北京:化学工业出版社,2006.
[3]毕书东.多目标优化遗传算法的研究[D].硕士论文.安徽:安徽理工大学,2007.
[4]雷英杰,张善文,李续武,周创明.MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005.
[5]赖鑫生.神经网络结合遗传算法优化应用[J].贵州大学学报(自然科学版),2004,21(2).
[6]杨洪山,汤晶,陈家训.应用神经网络和遗传算法实现多变量离散系统优化[J].计算机仿真,2005,22(12).
[7]吴吉平,赵异波,熊勇刚.机械CAD参数化设计技术[J].机械科学与技术,1999,18(6)
[8]刘淑香.集装箱龙门起重机结构系统动态优化设计[D].硕士论文.成都:西南交通大学,2007.
[9]中华人民共和国国家标准GB/T3811-83起重机设计规范[S].北京:中国标准出版社,1983
[10]王金诺,于兰峰.起重运输机械金属结构[M].北京:中国铁道出版社,2002.
[11]刘鸿文.简明材料力学[M].北京:高等教育出版社,1995.
[12]博弈创作室.APDL参数化有限元分析技术及其应用实例[M].北京:中国水利水电出版利,2004.
[13]张质文,虞和谦,王金诺,包起帆等.起重机设计手册.北京:中国铁道出版社,1998
[14]梁尚明,殷国富.现代机械优化设计方法[M].北京:化学工业出版社,2005.
[15]杨丹.轨道集装箱龙门起重机结构运动数值仿真及动力学分析[D].硕士论文.武汉:武汉理工大学,2006.
[16]博弈创作室.ANSYS 9.0经典产品高级分析技术与实例详解[M].北京:中国水利水电出版利,2005.
[17]于兰峰.塔式起重机结构刚性及动态优化研究[D].博士论文.成都:西南交通大学,2007.
[18]张晓丽,李德建,张雄.门式起重机静动力特性分析[J].重庆工学院学报,2006,20(5):29-32.
[19]蒋宗礼.人工神经网络导论[M].北京:高等教育出版社,2001.
[20]朱大奇,史慧.人工神经网络原理及应用[M].北京:科学出版社,2006.
[21]飞思科技产品研发中心.神经网络理论与MATLAB 7实现[M].北京:电子工业出版社,2005.
[22]韩力群.人工神经网络教程[M].北京:北京邮电大学出版社,2006.
[23]阎平凡,张长水.人工神经网络与模拟进化计算(第2版)[M].北京:清华大学出版社,2005.
[24]苑希民,李鸿雁,刘树坤,崔广涛等.神经网络和遗传算法在水科学领域的应用[M].北京:中国水利水电出版社,2002.
[25]韩力群.人工神经网络理论、设计及应用--人工神经细胞、人工神经网络和人工神经系统[M].北京:化学工业出版社,2002.
[26]闻新,周露,王丹力,熊晓英.MATLAB神经网络应用设计[M].北京:科学出版社,2002.
[27]周开利,康耀红.经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2005.
[28]王小平,曹立明.遗传算法--理论、应用与软件实现[M].西安:西安交通大学出版社,2002.
[29]陈伦军等.机械优化设计遗传算法[M].北京:机械工业出版社,2005.
[30]董长虹.Matlab神经网络与应用[M].北京:国防工业出版社,2005.
[31]Preton.T.W.The finite element method as a computational tool for design[J].IEE Colloquium,1992,1(22):1-6.
[32]Schaffer J D.Multi-objective optimization with vector evaluated genetic algorithms.Proceedings of the 1st International Conference on Genetic Algorithms,Lawrence Erlbaum,1985:93-100
[33]Davis L(ed.).Handbook of genetic algorithms[M].Van Nostrand Reinhold,New York,1991
[34]Jenkins W.M.Structural optimization with the genetic algorithms[J].Structural Engineer,1991,69(24):408-422
[35]Valenzuela Rendon and Uresti Charre.A non-generational genetic algorithm for Multi-objective optimization[J].Proceedings of the Seventh International Conference on Genetic Algorithms,San Francisco,California,Morgan Kaufmann,1997:658-665
[36]陈国良,庄镇泉,王东生.遗传算法及其应用[M].北京:人民邮电出版社,1996.
[37]Carlos A.Coello Coello.A comprehensive survey of evolutionary-based multi-objective optimization techniques[J].Knowledge and Information Systems.1999,1(3):269-308
[38]M.Geradin,D.Rixen.Mechanical Vibrations:Theory and application to structural dynamics[M].2nd Edition,1999.
[39]高隽.人工神经网络原理及仿真实例[M].北京:机械工业出版社,2003.
[40]金桥.基于人工神经网络算法的港口物流需求预测研究及实证分析[D].硕士论文.北京:北京交通大学,2008
[41]Joshua D.Knowles and David W.Corne.The Pareto Archived Evolution Strategy:A New Baseline Algorithm for Multi-objective optimization.In 1999 Congress on Evolutionary Computation.Washington D.C.July 1999,98-105
[42]J.H.Holland.Adaptation in Natural and Artificial Systems[M].Univ.of Michigan Press,Ann Arbor,1975
[43]D.E.Goldberg.Genetic Algorithms in Search,Optimization and Machine Learning[M].Addison-Wesley,1989
[44]涂雪珠.遗传算法在多目标优化中的应用[D].硕士论文.福建:福州大学,2004
[45]陈海霞.钢筋混凝土框架结构的优化设计[D].硕士论文.太原:太原理工大学,2008.
[46]魏锋涛.基于改进遗传算法的机械多目标优化设计方法的研究[D].硕士论文.西安:西安理工大学,2006
[47]N.Srinivas and Kalyanmoy Deb.Multi-objective Optimization Using the Nondominated Sorting in Genetic Algorithms[J].Evolutionary Computation.1994,2(3):221-248
[48]王达.遗传算法用于多目标过程优化综合的研究[D].硕士论文.青岛:青岛科技大学,2005
[49]谢涛,陈火旺.多目标优化与决策问题的演化算法.中国工程科学[J],2002年第4卷第2期
[2]黄大巍,李风,毛文杰.现代起重运输机械[M].北京:化学工业出版社,2006.
[3]毕书东.多目标优化遗传算法的研究[D].硕士论文.安徽:安徽理工大学,2007.
[4]雷英杰,张善文,李续武,周创明.MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005.
[5]赖鑫生.神经网络结合遗传算法优化应用[J].贵州大学学报(自然科学版),2004,21(2).
[6]杨洪山,汤晶,陈家训.应用神经网络和遗传算法实现多变量离散系统优化[J].计算机仿真,2005,22(12).
[7]吴吉平,赵异波,熊勇刚.机械CAD参数化设计技术[J].机械科学与技术,1999,18(6)
[8]刘淑香.集装箱龙门起重机结构系统动态优化设计[D].硕士论文.成都:西南交通大学,2007.
[9]中华人民共和国国家标准GB/T3811-83起重机设计规范[S].北京:中国标准出版社,1983
[10]王金诺,于兰峰.起重运输机械金属结构[M].北京:中国铁道出版社,2002.
[11]刘鸿文.简明材料力学[M].北京:高等教育出版社,1995.
[12]博弈创作室.APDL参数化有限元分析技术及其应用实例[M].北京:中国水利水电出版利,2004.
[13]张质文,虞和谦,王金诺,包起帆等.起重机设计手册.北京:中国铁道出版社,1998
[14]梁尚明,殷国富.现代机械优化设计方法[M].北京:化学工业出版社,2005.
[15]杨丹.轨道集装箱龙门起重机结构运动数值仿真及动力学分析[D].硕士论文.武汉:武汉理工大学,2006.
[16]博弈创作室.ANSYS 9.0经典产品高级分析技术与实例详解[M].北京:中国水利水电出版利,2005.
[17]于兰峰.塔式起重机结构刚性及动态优化研究[D].博士论文.成都:西南交通大学,2007.
[18]张晓丽,李德建,张雄.门式起重机静动力特性分析[J].重庆工学院学报,2006,20(5):29-32.
[19]蒋宗礼.人工神经网络导论[M].北京:高等教育出版社,2001.
[20]朱大奇,史慧.人工神经网络原理及应用[M].北京:科学出版社,2006.
[21]飞思科技产品研发中心.神经网络理论与MATLAB 7实现[M].北京:电子工业出版社,2005.
[22]韩力群.人工神经网络教程[M].北京:北京邮电大学出版社,2006.
[23]阎平凡,张长水.人工神经网络与模拟进化计算(第2版)[M].北京:清华大学出版社,2005.
[24]苑希民,李鸿雁,刘树坤,崔广涛等.神经网络和遗传算法在水科学领域的应用[M].北京:中国水利水电出版社,2002.
[25]韩力群.人工神经网络理论、设计及应用--人工神经细胞、人工神经网络和人工神经系统[M].北京:化学工业出版社,2002.
[26]闻新,周露,王丹力,熊晓英.MATLAB神经网络应用设计[M].北京:科学出版社,2002.
[27]周开利,康耀红.经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2005.
[28]王小平,曹立明.遗传算法--理论、应用与软件实现[M].西安:西安交通大学出版社,2002.
[29]陈伦军等.机械优化设计遗传算法[M].北京:机械工业出版社,2005.
[30]董长虹.Matlab神经网络与应用[M].北京:国防工业出版社,2005.
[31]Preton.T.W.The finite element method as a computational tool for design[J].IEE Colloquium,1992,1(22):1-6.
[32]Schaffer J D.Multi-objective optimization with vector evaluated genetic algorithms.Proceedings of the 1st International Conference on Genetic Algorithms,Lawrence Erlbaum,1985:93-100
[33]Davis L(ed.).Handbook of genetic algorithms[M].Van Nostrand Reinhold,New York,1991
[34]Jenkins W.M.Structural optimization with the genetic algorithms[J].Structural Engineer,1991,69(24):408-422
[35]Valenzuela Rendon and Uresti Charre.A non-generational genetic algorithm for Multi-objective optimization[J].Proceedings of the Seventh International Conference on Genetic Algorithms,San Francisco,California,Morgan Kaufmann,1997:658-665
[36]陈国良,庄镇泉,王东生.遗传算法及其应用[M].北京:人民邮电出版社,1996.
[37]Carlos A.Coello Coello.A comprehensive survey of evolutionary-based multi-objective optimization techniques[J].Knowledge and Information Systems.1999,1(3):269-308
[38]M.Geradin,D.Rixen.Mechanical Vibrations:Theory and application to structural dynamics[M].2nd Edition,1999.
[39]高隽.人工神经网络原理及仿真实例[M].北京:机械工业出版社,2003.
[40]金桥.基于人工神经网络算法的港口物流需求预测研究及实证分析[D].硕士论文.北京:北京交通大学,2008
[41]Joshua D.Knowles and David W.Corne.The Pareto Archived Evolution Strategy:A New Baseline Algorithm for Multi-objective optimization.In 1999 Congress on Evolutionary Computation.Washington D.C.July 1999,98-105
[42]J.H.Holland.Adaptation in Natural and Artificial Systems[M].Univ.of Michigan Press,Ann Arbor,1975
[43]D.E.Goldberg.Genetic Algorithms in Search,Optimization and Machine Learning[M].Addison-Wesley,1989
[44]涂雪珠.遗传算法在多目标优化中的应用[D].硕士论文.福建:福州大学,2004
[45]陈海霞.钢筋混凝土框架结构的优化设计[D].硕士论文.太原:太原理工大学,2008.
[46]魏锋涛.基于改进遗传算法的机械多目标优化设计方法的研究[D].硕士论文.西安:西安理工大学,2006
[47]N.Srinivas and Kalyanmoy Deb.Multi-objective Optimization Using the Nondominated Sorting in Genetic Algorithms[J].Evolutionary Computation.1994,2(3):221-248
[48]王达.遗传算法用于多目标过程优化综合的研究[D].硕士论文.青岛:青岛科技大学,2005
[49]谢涛,陈火旺.多目标优化与决策问题的演化算法.中国工程科学[J],2002年第4卷第2期