Genetic algorithm-aided reliability analysis

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.