多目标遗传算法与其在管路卡箍布局优化设计中的应用-研究

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1、武汉理工大学硕士学位论文bearingstructure.AndthemodifiedMOGAisappliedinoptimaldesignofpipelinetogetParetooptimalsolutions.Thenthemodalanalysisisadoptedtoselecttheoptimaldesignparametersinaero-enginepipelinesystemasanauxiliary.Inthispaper,wemodifythemulti-objectivegeneticalgorithm.Andapplytoaero-e

2、nginepipingsystemoptimizationdesign.Werealizepreliminarypipingoptimizationdesignusinggeneticalgorithm,improvethedesignefficiencyofpipingsystem.Keywords:multi-objectivegeneticalgorithm,pipingclamp,optimaldesign,fittingdivideIII万方数据武汉理工大学硕士学位论文目录第1章绪论...................................

3、............................................................................11.1课题来源.........................................................................................................11.2课题研究的背景及目的意义.........................................................................11.3相关

4、领域研究现状.........................................................................................21.3.1多目标遗传算法在优化设计中的应用研究现状...............................21.3.2管路卡箍优化设计研究现状...............................................................51.4本文的主要研究内容与组织结构.................................

5、................................71.4.1论文的主要研究内容...........................................................................71.4.2论文的组织结构...................................................................................8第2章管路卡箍布局优化设计建模及方法选择.............................................

6、....102.1多目标优化设计问题描述...........................................................................102.2管路卡箍布局优化设计模型的建立.........................................................122.2.1管路卡箍布局优化设计目标函数.....................................................132.2.2管路卡箍布局优化设计变量...................

7、..........................................142.2.3管路卡箍布局优化设计约束条件.....................................................152.3管路卡箍布局优化设计方法选取...............................................................162.3.1传统多目标优化设计方法.................................................................162

8、.3.2多目标遗传算法.