论文标题
基于二进制网络可靠性近似的基于自适应二进制二进制算术算法算法的新型蒙特卡洛模拟
Self-Adaptive Binary-Addition-Tree Algorithm-Based Novel Monte Carlo Simulation for Binary-State Network Reliability Approximation
论文作者
论文摘要
蒙特卡洛模拟（MCS）是在大量应用中使用的一种统计方法。它使用重复的随机抽样来解决概率解释的问题，以获得高质量的数值结果。 MCS简单易于开发，实施和应用。但是，其计算成本和总运行时可能很高，因为它需要许多样品才能获得差异较低的准确近似值。在本文中，基于二进制适应性-tree算法（BAT）的新型MC，称为自适应BAT-MCS，并提出了我们提出的自适应模拟算法，以简单有效地减少MCS的运行时间和方差。提出的自适应BAT-MC被应用于简单的基准问题，以证明其在网络可靠性中的应用。讨论了统计特征，包括期望，方差和仿真数，以及提出的自适应BAT-MC的时间复杂性。此外，在大规模问题上，其性能与传统MC的性能进行了比较。
The Monte Carlo simulation (MCS) is a statistical methodology used in a large number of applications. It uses repeated random sampling to solve problems with a probability interpretation to obtain high-quality numerical results. The MCS is simple and easy to develop, implement, and apply. However, its computational cost and total runtime can be quite high as it requires many samples to obtain an accurate approximation with low variance. In this paper, a novel MCS, called the self-adaptive BAT-MCS, based on the binary-adaption-tree algorithm (BAT) and our proposed self-adaptive simulation-number algorithm is proposed to simply and effectively reduce the run time and variance of the MCS. The proposed self-adaptive BAT-MCS was applied to a simple benchmark problem to demonstrate its application in network reliability. The statistical characteristics, including the expectation, variance, and simulation number, and the time complexity of the proposed self-adaptive BAT-MCS are discussed. Furthermore, its performance is compared to that of the traditional MCS extensively on a large-scale problem.
