A Cut-Based BAT-MCS Approach for Binary-State Network Reliability Assessment
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909506046263296 |
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| author | Yeh, Wei-Chang |
| author_facet | Yeh, Wei-Chang |
| contents | The BAT-MCS is an integrated Monte Carlo simulation method (MCS) that combines a binary adaptation tree algorithm (BAT) with a self-regulating simulation mechanism. The BAT algorithm operates deterministically, while the Monte Carlo simulation method is stochastic. By hybridizing these two approaches, BAT-MCS successfully reduces variance, increases efficiency, and improves the quality of its binary-state network reliability. However, it has two notable weaknesses. First, the selection of the supervectors, sub-vectors that form the core of BAT-MCS, is overly simplistic, potentially affecting overall performance. Second, the calculation of the approximate reliability is complicated, which limits its strength in reducing variance. In this study, a new BAT-MCS called cBAT-MCS is proposed to enhance the performance of the BAT-MCS. The approach reduces the complexity of MCS. Selecting the super-vector based on a novel layer-cut approach can reduce both runtime and variance. Extensive numerical experiments on large-scale binary-state network demonstrate that the proposed new cBAT-MCS outperforms traditional MCS and original BAT-MCS approaches in terms of computational efficiency and accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16224 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Cut-Based BAT-MCS Approach for Binary-State Network Reliability Assessment Yeh, Wei-Chang Computational Engineering, Finance, and Science Numerical Analysis The BAT-MCS is an integrated Monte Carlo simulation method (MCS) that combines a binary adaptation tree algorithm (BAT) with a self-regulating simulation mechanism. The BAT algorithm operates deterministically, while the Monte Carlo simulation method is stochastic. By hybridizing these two approaches, BAT-MCS successfully reduces variance, increases efficiency, and improves the quality of its binary-state network reliability. However, it has two notable weaknesses. First, the selection of the supervectors, sub-vectors that form the core of BAT-MCS, is overly simplistic, potentially affecting overall performance. Second, the calculation of the approximate reliability is complicated, which limits its strength in reducing variance. In this study, a new BAT-MCS called cBAT-MCS is proposed to enhance the performance of the BAT-MCS. The approach reduces the complexity of MCS. Selecting the super-vector based on a novel layer-cut approach can reduce both runtime and variance. Extensive numerical experiments on large-scale binary-state network demonstrate that the proposed new cBAT-MCS outperforms traditional MCS and original BAT-MCS approaches in terms of computational efficiency and accuracy. |
| title | A Cut-Based BAT-MCS Approach for Binary-State Network Reliability Assessment |
| topic | Computational Engineering, Finance, and Science Numerical Analysis |
| url | https://arxiv.org/abs/2502.16224 |