Genetic algorithm-aided reliability analysis

N Harnpornchai

Journal of Risk and Reliability, 2011, vol. 225, issue 1, 62-80

Abstract: A hybrid procedure consisting of the combination of a genetic algorithm (GA) and reliability analysis (referred to as GA-aided reliability analysis) is described, discussed, and summarized. Two classes of GA, namely simple GAs and multimodal GAs, are introduced to solve a number of important problems in reliability analysis. The problems cover the determination of the point of maximum likelihood (PML) in the failure domain, the computation of failure probability using the GA-determined PML, and the determination of multiple design points. The Monte Carlo simulation-based (MCS-based) method using the GA-determined PML is specifically implemented in the so-called importance sampling around PML (ISPML). The application of the GA-based approach to several problems is then demonstrated via numerical examples. With the aid of GAs, an accurate reliability analysis can be achieved even if there is no information about either the geometry of the limit state surfaces or the total number of crucial likelihood points. In addition, GAs significantly improve the computational efficiency and increase the potential of rare event analysis under the condition of limited computational resources. The implementation of the GA-based approaches is straightforward due to their algorithmic simplicity.

Keywords: genetic algorithms; reliability analysis; simulation methods; complex systems; rare-event analysis; multiple failure modes (search for similar items in EconPapers)
Date: 2011
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1748006XJRR302 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:225:y:2011:i:1:p:62-80

DOI: 10.1177/1748006XJRR302

Access Statistics for this article

More articles in Journal of Risk and Reliability
Bibliographic data for series maintained by SAGE Publications ().