基于ANSYS平台的遗传算法在结构优化设计中的应用
摘要
随着科学的发展和社会的进步,特别是计算机的迅速发展,人们对高效的优化设计技术和智能计算的要求日益迫切。结构优化设计理论已有近四十年的发展历史,很多优化算法已相当成熟。近些年,一种新的优化方法——遗传算法(简称GA)正迅速发展,该算法根植于自然进化与遗传机理,是用于模拟自然界的自适应现象,具有不依赖于问题模型的特性、全局最优性、隐含并行性、高效率及解决不同非线性问题的强鲁棒性等优点。本文主要做了以下研究内容:
(1)本文对遗传算法的基本概念、理论及其在结构优化设计应用中的相关内容进行了分析和综合。对遗传算法应用于结构优化设计的数学建模、约束条件处理、初始种群的产生及遗传算法控制参数的选择等几个关键因素作了分析和研究。
(2)本文针对简单遗传算法具有“收敛早熟”、局部搜索能力差等缺点,提出了改进的遗传算法,让初始群体不止一个,使初始群体多样化,这样可以避免出现早熟现象,提高优化质量和效率。
(3)本文利用遗传算法对结构进行优化时,将ANSYS作为有限元结构分析工具,替代人工编制的有限元程序。将遗传算法优化程序与结构的有限元分析程序结合起来,在这两个程序中进行数据传递。这样,遗传算法与ANSYS结构分析软件相结合能大大节约结构优化问题的求解时间,提高计算结果的可靠性。
(4)本文将遗传算法与ANSYS相结合,对两种典型结构进行最优化计算,计算结果表明此方法正确、可行,能充分发挥ANSYS与遗传算法两者的长处,具有优化效率高、效果好、实用性强等优点。
With the development of the science and the progress of the society, especially, due to the fast development of computers, high effective technology of optimum structure design and intelligent computation are in urgently needed. The theories of optimum structure design have developed for forty years, and many optimum methods are quite mature. In these years, a new optimum method, genetic algorithm, is rapidly developing. The method, originating from natural evolution and genetic mechanism and simulating auto-adapted phenomenon of nature, has merits of no relying on the question model, overall optimization, concealment parallelism, high efficiency, solving different non-linear problems, etc. This paper mainly makes these reseach:
(1) This paper analyzes and synthesizes genetic algorithm's basic concepts, theory and its relative application contents in structural optimization. The paper introduces basic theory of structural optimization and other optimum methods; it analyzes and studies some key factors in GA's application, such as, mathematics modeling, processing restraint condition, generation of initial population, choice of controlling parameters, etc.
(2) Simple genetic algorithm is characterized by"Premature Convergence (PC)" and weakness in local search. Considering the disadvantage of GA, this paper proposes to improve genetic algorithm, diversifying the initial populations, to avoid PC and improve optimization efficiency and quality.
(3) This paper uses ANSYS instead of compiled procedure to optimize structures, to combine genetic algorithm optimization procedure with the structure finite element analysis procedure, making the data transmit between the two procedures. Thus, the combination can save a lot of time and improve the reliability of result.
(4) This paper combines genetic algorithm with ANSYS to optimize computation of the two kind typical structures. The result indicates this method, being correct and feasible, can fully display the merits of both ANSYS and the genetic algorithm. All in all, higher efficiency, better result and stranger usability are their merit.
(1)本文对遗传算法的基本概念、理论及其在结构优化设计应用中的相关内容进行了分析和综合。对遗传算法应用于结构优化设计的数学建模、约束条件处理、初始种群的产生及遗传算法控制参数的选择等几个关键因素作了分析和研究。
(2)本文针对简单遗传算法具有“收敛早熟”、局部搜索能力差等缺点,提出了改进的遗传算法,让初始群体不止一个,使初始群体多样化,这样可以避免出现早熟现象,提高优化质量和效率。
(3)本文利用遗传算法对结构进行优化时,将ANSYS作为有限元结构分析工具,替代人工编制的有限元程序。将遗传算法优化程序与结构的有限元分析程序结合起来,在这两个程序中进行数据传递。这样,遗传算法与ANSYS结构分析软件相结合能大大节约结构优化问题的求解时间,提高计算结果的可靠性。
(4)本文将遗传算法与ANSYS相结合,对两种典型结构进行最优化计算,计算结果表明此方法正确、可行,能充分发挥ANSYS与遗传算法两者的长处,具有优化效率高、效果好、实用性强等优点。
With the development of the science and the progress of the society, especially, due to the fast development of computers, high effective technology of optimum structure design and intelligent computation are in urgently needed. The theories of optimum structure design have developed for forty years, and many optimum methods are quite mature. In these years, a new optimum method, genetic algorithm, is rapidly developing. The method, originating from natural evolution and genetic mechanism and simulating auto-adapted phenomenon of nature, has merits of no relying on the question model, overall optimization, concealment parallelism, high efficiency, solving different non-linear problems, etc. This paper mainly makes these reseach:
(1) This paper analyzes and synthesizes genetic algorithm's basic concepts, theory and its relative application contents in structural optimization. The paper introduces basic theory of structural optimization and other optimum methods; it analyzes and studies some key factors in GA's application, such as, mathematics modeling, processing restraint condition, generation of initial population, choice of controlling parameters, etc.
(2) Simple genetic algorithm is characterized by"Premature Convergence (PC)" and weakness in local search. Considering the disadvantage of GA, this paper proposes to improve genetic algorithm, diversifying the initial populations, to avoid PC and improve optimization efficiency and quality.
(3) This paper uses ANSYS instead of compiled procedure to optimize structures, to combine genetic algorithm optimization procedure with the structure finite element analysis procedure, making the data transmit between the two procedures. Thus, the combination can save a lot of time and improve the reliability of result.
(4) This paper combines genetic algorithm with ANSYS to optimize computation of the two kind typical structures. The result indicates this method, being correct and feasible, can fully display the merits of both ANSYS and the genetic algorithm. All in all, higher efficiency, better result and stranger usability are their merit.
引文
[1]张炳华等编.土建结构优化设计[M].上海.同济大学出版社,1988.1-9。
[2]李炳威.结构优化设计[M].北京.人民交通出版社,1989.1-10。
[3]王光远,董明耀.结构优化设计[M].北京.高等教育出版社,1987.1-25。
[4]汪玉树,刘国华,包志仁.结构优化设计的现状与进展[J].基建优化,1999,20(4):3.
[5]李芳,凌道盛.工程结构优化设计发展综述[J].工程设计学报,2002,9(5):229-233.
[6]朱伯芳,黎展眉,张璧城.结构优化设计原理与应用[M].北京.水利电力出版社出版,1984.1-10.
[7]江爱川.结构优化设计[M].北京.清华大学出版社,1986,1-10.
[8]E.J.Haug and J.S.Arora,Applied Optimal Design,John Wiley & Sons,1979.1-10.
[9]雷英杰等.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,2005.1-61.
[10]S.S.雷欧,著,祁载康,万耀青,梁嘉玉,译,工程优化原理及应用[M].北京.北京大学出版社,1990,1-50.
[11]马少杰,刘国东,王水非等.基于人工神经网络的工程结构优化设计研究[J].工程建设与设计,2006.1,16-18.
[12]李敏强.遗传算法的基本理论与应用[M].北京:科学出版社,2002.
[13]张文修,张怡.遗传算法的数学基础[M].西安:西安交通大学出版社,2000.
[14]施光林,史维祥.遗传算法及其研究与应用新进展[J].科技导报,1999,4:36-37.
[15]董列奎.遗传算法在建筑结构优化设计中的应用研究[D].2003,1-34.
[16]孙刚.基于ANSYS平台的改进遗传算法在结构优化中的研究与探讨[D].2005,1-50.
[17]Rudolph,G.Convergence analysis of canonical genetic algorithms.IEEE Transactions on Neural Networks,1994,5(1):86-101.
[18]Goldberg,D.E.,Korb B.and Deb K.Messy genetic algorithms:motivation,analysis,and first results.Complex systems,1989,3:493-530.
[19]Liepins,G.E.and Vose,M.D.Representational issues in genetic optimization.Journal of Experimental and Theoretical Artificial Intelligence,1990,2:4-30.
[20]Schraudolph,N.N.and Belew,R.K.Dynamic parameter encoding for genetic algorithms.Machine Leaning,1992,9(1):9-12.
[21]鹿跃丽,周力平.遗传算法用于工程结构优化设计的研究[J].郑州工业大学学报,1998,19(2):35-39.
[22]张延年,刘斌,董锦坤,郭鹏飞.改进遗传算法在建筑结构优化设计中的应用[J].东北 大学学报,2004,25(7):692-694.
[23]王宏丽,李凯,李良晨.基于遗传算法的框架结构的优化设计[J].西北农林科技大学学报,2001,29(5):137-140.
[24]张文元,吴知丰.遗传算法在建筑结构优化设计中的应用[J].哈尔滨建筑大学学报,1999,32(4):18-23.
[25]李娜,祝晓燕.自适应遗传算法在桁架截面优化设计中的应用[J].煤矿机械,2005,11:28-30.
[26]何飞,程志毅,刘亚.改进的遗传算法在优化中的应用[J].湖北工学院学报,2003,18(2):8-10.
[27]肖伟,全慧云,史滋福.改进的遗传算法[J].计算机工程与应用,2004,4:53-55.
[28]曾毅.改进的遗传算法在非线性方程组求解中的应用[J].华东交通大学学报,2004,21(4):132-134.
[29]王云仓.基于ANSYS的结构优化设计方法[J].水科学与工程技术,2005,增刊:11-12.
[30]黄富洪,李泉永,袭雨兵等.基于ANSYS的结构优化设计[J].桂林电子工业学院学报,2005,25(2):23-26.
[31]郝金伟,闫弈任等.基于ANSYS的结构优化设计有限元分析[J].山西建筑,2005,31(5):31
[32]Youngv,Querinom,Stevengp.3D and multiple load case bi-directional evolutionary structural optimization(BESO)[J].Structural Optimization 1999,18:183-192.
[33]任重.ANSYS实用分析教程[M].北京:北京大学出版社,2003,285-333.
[34]刑静忠,王永岗等.ANSYS7.0分析实例于工程应用[M].北京:机械工业出版社,2004,440-452.
[35]姜勇,张波.ANSYS7.0实例精解[M].北京:清华大学出版社,2004,447-453.
[36]李福强,胡卫兵.基于ANSYS的结构优化设计[J].第16届全国结构工程学术会议论文集.北京:工程力学杂志社,2007,(Ⅰ):464-467.
[37]黎钧琪,石国桢.遗传算法交叉率与变异率关系的研究[J].武汉理工大学学报,2003,27(1),97-99.
[2]李炳威.结构优化设计[M].北京.人民交通出版社,1989.1-10。
[3]王光远,董明耀.结构优化设计[M].北京.高等教育出版社,1987.1-25。
[4]汪玉树,刘国华,包志仁.结构优化设计的现状与进展[J].基建优化,1999,20(4):3.
[5]李芳,凌道盛.工程结构优化设计发展综述[J].工程设计学报,2002,9(5):229-233.
[6]朱伯芳,黎展眉,张璧城.结构优化设计原理与应用[M].北京.水利电力出版社出版,1984.1-10.
[7]江爱川.结构优化设计[M].北京.清华大学出版社,1986,1-10.
[8]E.J.Haug and J.S.Arora,Applied Optimal Design,John Wiley & Sons,1979.1-10.
[9]雷英杰等.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,2005.1-61.
[10]S.S.雷欧,著,祁载康,万耀青,梁嘉玉,译,工程优化原理及应用[M].北京.北京大学出版社,1990,1-50.
[11]马少杰,刘国东,王水非等.基于人工神经网络的工程结构优化设计研究[J].工程建设与设计,2006.1,16-18.
[12]李敏强.遗传算法的基本理论与应用[M].北京:科学出版社,2002.
[13]张文修,张怡.遗传算法的数学基础[M].西安:西安交通大学出版社,2000.
[14]施光林,史维祥.遗传算法及其研究与应用新进展[J].科技导报,1999,4:36-37.
[15]董列奎.遗传算法在建筑结构优化设计中的应用研究[D].2003,1-34.
[16]孙刚.基于ANSYS平台的改进遗传算法在结构优化中的研究与探讨[D].2005,1-50.
[17]Rudolph,G.Convergence analysis of canonical genetic algorithms.IEEE Transactions on Neural Networks,1994,5(1):86-101.
[18]Goldberg,D.E.,Korb B.and Deb K.Messy genetic algorithms:motivation,analysis,and first results.Complex systems,1989,3:493-530.
[19]Liepins,G.E.and Vose,M.D.Representational issues in genetic optimization.Journal of Experimental and Theoretical Artificial Intelligence,1990,2:4-30.
[20]Schraudolph,N.N.and Belew,R.K.Dynamic parameter encoding for genetic algorithms.Machine Leaning,1992,9(1):9-12.
[21]鹿跃丽,周力平.遗传算法用于工程结构优化设计的研究[J].郑州工业大学学报,1998,19(2):35-39.
[22]张延年,刘斌,董锦坤,郭鹏飞.改进遗传算法在建筑结构优化设计中的应用[J].东北 大学学报,2004,25(7):692-694.
[23]王宏丽,李凯,李良晨.基于遗传算法的框架结构的优化设计[J].西北农林科技大学学报,2001,29(5):137-140.
[24]张文元,吴知丰.遗传算法在建筑结构优化设计中的应用[J].哈尔滨建筑大学学报,1999,32(4):18-23.
[25]李娜,祝晓燕.自适应遗传算法在桁架截面优化设计中的应用[J].煤矿机械,2005,11:28-30.
[26]何飞,程志毅,刘亚.改进的遗传算法在优化中的应用[J].湖北工学院学报,2003,18(2):8-10.
[27]肖伟,全慧云,史滋福.改进的遗传算法[J].计算机工程与应用,2004,4:53-55.
[28]曾毅.改进的遗传算法在非线性方程组求解中的应用[J].华东交通大学学报,2004,21(4):132-134.
[29]王云仓.基于ANSYS的结构优化设计方法[J].水科学与工程技术,2005,增刊:11-12.
[30]黄富洪,李泉永,袭雨兵等.基于ANSYS的结构优化设计[J].桂林电子工业学院学报,2005,25(2):23-26.
[31]郝金伟,闫弈任等.基于ANSYS的结构优化设计有限元分析[J].山西建筑,2005,31(5):31
[32]Youngv,Querinom,Stevengp.3D and multiple load case bi-directional evolutionary structural optimization(BESO)[J].Structural Optimization 1999,18:183-192.
[33]任重.ANSYS实用分析教程[M].北京:北京大学出版社,2003,285-333.
[34]刑静忠,王永岗等.ANSYS7.0分析实例于工程应用[M].北京:机械工业出版社,2004,440-452.
[35]姜勇,张波.ANSYS7.0实例精解[M].北京:清华大学出版社,2004,447-453.
[36]李福强,胡卫兵.基于ANSYS的结构优化设计[J].第16届全国结构工程学术会议论文集.北京:工程力学杂志社,2007,(Ⅰ):464-467.
[37]黎钧琪,石国桢.遗传算法交叉率与变异率关系的研究[J].武汉理工大学学报,2003,27(1),97-99.