Hierarchical riliability optimization using competent genetic algorithms

Recently, unprecedented growth in system complexity and miniaturization has led to many hierarchical reliability structures in system reliability. Additionally, nanotechnology will enable redundancy at any level of hierarchy for optimizing system reliability. However, most of the existing research works in reliability optimization of system design are limited to Parallel-series, General Network, k-out-of-n: G(F) and other system. No significant work has been done so far to optimize hierarchical redundancy allocation for improving system reliability. Taking into consideration of the industrial importance of hierarchical reliability system, this paper proposes a hierarchical series redundancy model. In this model, redundancy can be allocated to different level of hierarchy without any restriction. The proposed model having such freedom in redundancy allocation cannot be solved using simple genetic algorithm. To solve this hierarchical series redundancy optimization, a competent genetic algorithm, termed as Hierarchical Genetic Algorithm (HGA), has been used to solve hierarchical series redundancy optimization. The design variables have been coded as hierarchical genotype. The result achieved in this paper is near global optimal, because all possible combinations of optimal redundancy were achieved using HGA.