Article(id=1242129020490416290, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242129015037825607, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2012.18.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1325692800000, receivedDateStr=2012-01-05, revisedDate=1339084800000, revisedDateStr=2012-06-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1340812800000, onlineDateStr=2012-06-28, pubDate=1340812800000, pubDateStr=2012-06-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1340812800000, onlineIssueDateStr=2012-06-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774076236120, creator=sys-migrate, updateTime=1774076236120, updator=sys-migrate, issue=Issue{id=1242129015037825607, tenantId=1146029695717560320, journalId=1146031591421210625, year='2012', volume='30', issue='18', pageStart='3', pageEnd='96', issueExtLink='null', onlineDate='null', pubDate='1340812800000', pubDateStr='2012-06-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774076234824, creator='sys-migrate', updateTime=1774076234824, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=32, endPage=36, ext={EN=ArticleExt(id=1242129024458232469, articleId=1242129020490416290, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Multi-mode Evolution Genetic Algorithm and Optimizaton of Thin-Walled Tube with Folding Patterns Baesd on Crashworthiness, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=A multi-mode evolution genetic algorithm is proposed to improve the simple genetic algorithm and to solve the crashworthiness optimization design problems for a new type of thin-walled tube with folding patterns. Structural crashworthiness analysis is a highly non-linear transient dynamic process, involving a very complex relationship between the objective function (energy absorption value) and the design variables (wave height and angle) without an explicit function expression to optimize the design of the thin-walled tube. The optimization model of the thin-walled tube established by the Response Surface Method (RSM) and various encoder modes, the selection strategy, the crossover and the mutation operation are used in the multi-mode evolution improved genetic algorithm, and an inversion operation, similar to the genetic variation in the chromosome structure, effectively improves the population diversity. Examples of function test results show that the algorithm overcomes the local convergence of the GA and increases the convergent speed. The application of the multi-mode evolution genetic algorithm for crashworthiness optimization of the thin-walled tube with folding patterns shows the efficiency and convenience of this algorithm to solve such optimization problems. The energy absorption of the optimized structure is increased by more than 40% , a significant improvement over the initial design., authors=SUN Hailong¹, SUI Yunkang^1,2, YE Hongling¹, authorsList=SUN Hailong;SUI Yunkang;YE Hongling, authorCompany=1. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100024, China;2. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology , Dalian 116023, Liaoning Province, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=lIPM1mJOeDefm+PV4txTig==, pdfFileSize=2110164, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242129023329960111, articleId=1242129020490416290, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=多方式进化遗传算法及其优化波纹薄壁管的耐撞性, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=通过提出一种多方式进化遗传算法的途径改进了遗传算法,并用于求解汽车新型波纹薄壁管耐撞性优化问题。文中采用响应面法近似建立金字塔形波纹薄壁管的优化模型,在多方式进化遗传算法中采用多种编码方式、选择策略、交叉和变异操作,同时还设计了类似遗传学中染色体结构变异的倒位操作,有效改善了群体多样性。对于函数实例测试的结果显示,该算法克服了遗传算法有时局部收敛的缺陷,提升了收敛速度。在波纹薄壁管耐撞性优化问题上的应用研究表明了本算法在求解此类优化问题上的有效性和方便性。优化后波纹管吸能提高40%以上,显著改进了初始设计,进一步验证了多方式进化遗传算法求解此类优化问题的实用性。, authors=孙海龙¹, 隋允康^1,2, 叶红玲¹, authorsList=孙海龙;隋允康;叶红玲, authorCompany=1. 北京工业大学机械工程与应用电子技术学院,北京 100024;2. 大连理工大学工业装备结构分析国家重点实验室,辽宁大连 116023, correspAuthors=null, authorNote=null, correspAuthorsNote=隋允康(中国科协所属全国学会个人会员登记号：S030000050S), 教授,研究方向为结构和多学科优化,电子信箱：ysui@bjut.edu.cn, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=XBpoX7fa9ZRg2edfRhCEVw==, pdfFileSize=2110164, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null)}, authors=null, 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