遗传算法在罕见事件可靠性分析中的应用

C. Suparattaya, N. Harnpornchai
{"title":"遗传算法在罕见事件可靠性分析中的应用","authors":"C. Suparattaya, N. Harnpornchai","doi":"10.1080/713926641","DOIUrl":null,"url":null,"abstract":"An application of Genetic Algorithms (GAs) to reliability analysis in rare events is presented. The application is directed towards the enhancement of the computational capability in the analysis. As an exemplified application, GAs are utilized for searching the point of maximum likelihood of failure probability, referred to as the design point. The design point is then used as the center of the sampling density in Monte Carlo Simulation (MCS) by which the computation of low probability becomes efficient. The most advantageous aspect of GAs is that the explicitness of limit-state-function in terms of basic random variables is not required in their search operation. The determination of the design point is thus made possible in the problems involved with complex systems where implicit limit-state-functions are naturally encountered. Consequently, the reliability analysis of broader classes of systems with the interest in rare events can be realized and efficiently accomplished by such an application of GAs.","PeriodicalId":212131,"journal":{"name":"Risk Decision and Policy","volume":"293 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An application of genetic algorithms to reliability analysis in rare events\",\"authors\":\"C. Suparattaya, N. Harnpornchai\",\"doi\":\"10.1080/713926641\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An application of Genetic Algorithms (GAs) to reliability analysis in rare events is presented. The application is directed towards the enhancement of the computational capability in the analysis. As an exemplified application, GAs are utilized for searching the point of maximum likelihood of failure probability, referred to as the design point. The design point is then used as the center of the sampling density in Monte Carlo Simulation (MCS) by which the computation of low probability becomes efficient. The most advantageous aspect of GAs is that the explicitness of limit-state-function in terms of basic random variables is not required in their search operation. The determination of the design point is thus made possible in the problems involved with complex systems where implicit limit-state-functions are naturally encountered. Consequently, the reliability analysis of broader classes of systems with the interest in rare events can be realized and efficiently accomplished by such an application of GAs.\",\"PeriodicalId\":212131,\"journal\":{\"name\":\"Risk Decision and Policy\",\"volume\":\"293 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Decision and Policy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/713926641\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Decision and Policy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/713926641","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

介绍了遗传算法在罕见事件可靠性分析中的应用。该应用旨在提高分析中的计算能力。作为一个示例应用,利用ga来搜索故障概率最大可能的点,称为设计点。然后将设计点作为蒙特卡罗模拟(MCS)中采样密度的中心,从而提高了低概率计算的效率。GAs最有利的方面是在其搜索操作中不需要极限状态函数在基本随机变量方面的显式。因此,设计点的确定在涉及复杂系统的问题中是可能的,其中隐式极限状态函数是自然遇到的。因此,通过这种应用,可以实现并有效地完成对更广泛类别的系统的可靠性分析,并对罕见事件感兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An application of genetic algorithms to reliability analysis in rare events
An application of Genetic Algorithms (GAs) to reliability analysis in rare events is presented. The application is directed towards the enhancement of the computational capability in the analysis. As an exemplified application, GAs are utilized for searching the point of maximum likelihood of failure probability, referred to as the design point. The design point is then used as the center of the sampling density in Monte Carlo Simulation (MCS) by which the computation of low probability becomes efficient. The most advantageous aspect of GAs is that the explicitness of limit-state-function in terms of basic random variables is not required in their search operation. The determination of the design point is thus made possible in the problems involved with complex systems where implicit limit-state-functions are naturally encountered. Consequently, the reliability analysis of broader classes of systems with the interest in rare events can be realized and efficiently accomplished by such an application of GAs.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信