Truncated importance sampling method based on optimization of mixed genetic algorithm

Feng Zhang, Zhenzhou Lu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A truncated importance sampling method is employed for the failure probability of parallel system with multiple failure modes. The mixed genetic optimization algorithm is chosen to search for the most probable failure point in the failure domain, and solve the approximate reliability index β of the parallel system. A β-sphere truncated importance sampling method for the failure probability of parallel system is consisted of β-sphere truncation and importance sampling probability function. Comparing with the successive sequential approximation approach and FORM, the presented method performs much more efficiently than Monte-Carlo method with higher accuracy, especially for the small failure probability.

Original languageEnglish
Pages (from-to)190-193
Number of pages4
JournalYingyong Lixue Xuebao/Chinese Journal of Applied Mechanics
Volume26
Issue number1
StatePublished - Mar 2009

Keywords

  • β-sphere sampling
  • Failure probability
  • Importance sampling
  • Mixed genetic algorithms

Fingerprint

Dive into the research topics of 'Truncated importance sampling method based on optimization of mixed genetic algorithm'. Together they form a unique fingerprint.

Cite this