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An Importance Based Algorithm for Reliability-Redundancy Allocation of Phased Mission Systems

机译:分阶段任务系统可靠性-冗余分配的基于重要性算法

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In engineering applications, there are systems that have both newly developed components with optimized reliability parameters and existed components with fixed reliability parameters. The reliability-redundancy allocation problem (RRAP) is raised to solve these cases by simultaneous optimizing components reliability and providing redundant components. However, most of the existed literature does not consider this kind of systems and they mainly focuses on series-parallel system, this paper propose a hybrid heuristic measure to solve RRAP of phased mission systems (PMS). Importance analysis can estimate the relative importance of components to system reliability, and provide useful information for reliability allocation or redundancy allocation for improving system performance. In this study, an importance based heuristic algorithm is hybridized with well-known genetic algorithm (GA). By embedding the reliability-importance based local search operator in standard GA, the local exploration ability has been further improved. Two PMS examples are presented and the results are compared with standard GA to validate the effectiveness of the provided algorithm.
机译:在工程应用中,有些系统既具有新开发的具有优化的可靠性参数的组件,又具有既具有固定的可靠性参数的组件。通过同时优化组件可靠性并提供冗余组件,提出了可靠性-冗余分配问题(RRAP)以解决这些情况。然而,现有文献中大多数都没有考虑这种系统,而是主要关注串联-并联系统,本文提出了一种混合启发式方法来解决相控任务系统(PMS)的RRAP问题。重要性分析可以估计组件对系统可靠性的相对重要性,并为可靠性分配或冗余分配提供有用的信息,以提高系统性能。在这项研究中,基于重要性的启发式算法与众所周知的遗传算法(GA)混合在一起。通过将基于重要性重要性的本地搜索运算符嵌入到标准GA中,本地搜索能力得到了进一步提高。给出了两个PMS示例,并将结果与​​标准GA进行了比较,以验证所提供算法的有效性。

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