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A hybrid enhanced bat algorithm for the generalized redundancy allocation problem

机译:一种用于广义冗余分配问题的混合增强蝙蝠算法

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摘要

A majority of existing works dealing with redundancy allocation problems are based on traditional series-parallel structures. While in many real-life scenarios, the way of connecting subsystems is not limited to a series-only configuration. This paper considers a generalized redundancy allocation problem (GRAP), where the system structure is a more general network. Since the reliability evaluation in GRAPs is a NP-hard problem and the traditional exact symbolic reliability calculation is not suitable, a cellular automata based monte carlo simulation method is implemented in this paper to estimate the system reliability. It is a relatively simple but effective method without knowing the MPs/MCs. Moreover, to deal with GRAPs, a novel discrete bat algorithm is proposed in this paper with a goal of determining an optimal system structure that achieves the minimum cost under several constraints by using redundant components in parallel. Computational complexity of the proposed algorithm is also calculated in this paper. In the end, three experiments are carried out based on ten networks to set parameters, measure the effectiveness of the modifications, and compare with other state-of-the-art algorithms, separately. The reported computational results show that the proposed algorithm is powerful, which is more superior on this sort of problems.
机译:处理冗余分配问题的大多数现有工作基于传统的串行结构结构。虽然在许多现实生活场景中,连接子系统的方式不仅限于唯一的配置。本文考虑了一个广义冗余分配问题(GRAP),系统结构是一个更通用的网络。由于涂鸦的可靠性评估是NP难题,并且传统的精确符号可靠性计算不合适,因此在本文中实施了一种基于蜂窝自动机的蒙特卡罗仿真方法,以估算系统可靠性。在不知道MPS / MCS的情况下,它是一种相对简单但有效的方法。此外,为了处理涂鸦,本文提出了一种新颖的离散BAT算法,目的是通过使用冗余组件并行地确定若干约束下实现最小成本的最佳系统结构。本文还计算了所提出的算法的计算复杂性。最后,基于十个网络进行三个实验来设置参数,测量修改的有效性,并与其他最先进的算法进行比较。报告的计算结果表明,该算法强大,这在这种问题上更优越。

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