Fuzzy Reliability Analysis Using Genetic Optimization Algorithm Combined with Adaptive Descent Chaos Control
Mansour Bagheri, Behrooz Keshtegar, Shun‐Peng Zhu, Debiao Meng, José A.F.O. Correia, Abílio M.P. De Jesus
Abstract
The robust result of analytical fuzzy reliability analysis (FRA) represents the main effort at evaluating the fuzzy reliability index. In this study, a bioloop-based hybrid method is proposed for structural FRA. The genetic operator as optimization solver combined with adaptive descent chaos control (ADCC) as a probabilistic solver called GA-ADCC is applied to evaluate the fuzzy reliability index. The ADCC-based reliability method is formulated based on a dynamical chaos control factor that is computed using an adaptive descent approach from the new and previous results. In GA-ADCC, an outer loop–based genetic optimizer constructs the membership reliability index using an alpha level set. To compute the membership functions of the reliability index, three structural problems are used to show the capability of the proposed method. Results demonstrate that the proposed GA-ADCC method can be used to evaluate reasonable uncertainty bounds in FRA, and it provides the accurate member shape functions for reliability index.