TITLE:
Reliability-Based Optimization: Small Sample Optimization Strategy
AUTHORS:
Drahomír Novák, Ondřej Slowik, Maosen Cao
KEYWORDS:
Optimization, Reliability Assessment, Aimed Multilevel Sampling, Monte Carlo, Latin Hypercube Sampling, Probability of Failure, Reliability-Based Design Optimization, Small Sample Analysis
JOURNAL NAME:
Journal of Computer and Communications,
Vol.2 No.11,
September
12,
2014
ABSTRACT: 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.