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文章基本信息

  • 标题:Reliability-Based Optimization: Small Sample Optimization Strategy
  • 本地全文:下载
  • 作者:Drahomír Novák 1* , Ondřej Slowik 1 , Maosen Cao
  • 期刊名称:Journal of Computer and Communications
  • 印刷版ISSN:2327-5219
  • 电子版ISSN:2327-5227
  • 出版年度:2014
  • 卷号:02
  • 期号:11
  • 页码:31-37
  • DOI:10.4236/jcc.2014.211004
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要:The aim of the paper is to present a newly developed approach for reliability-based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process of reliability-based optimization and reliability constrains are calculated in inner loop. Innovation of suggested approach is in application of newly developed optimization strategy based on multilevel simulation using an advanced Latin Hypercube Sampling technique. This method is called Aimed multilevel sampling and it is designated for optimization of problems where only limited number of simulations is possible to perform due to enormous com- putational demands.
  • 关键词:Optimization; Reliability Assessment; Aimed Multilevel Sampling; Monte Carlo; Latin Hypercube Sampling; Probability of Failure; Reliability-Based Design Optimization; Small Sample Analysis
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