让二进制加法树算法动态自适应，提升大规模网络可靠性计算效率
Applying Incremental Learning in Binary-Addition-Tree Algorithm for Dynamic Binary-State Network Reliability
- 用增量学习让静态的二进制加法树可随新数据迭代优化
- 计算效率与解质量显著优于传统算法和基于路径/割集的方法
- 适合需要实时更新的大型动态网络可靠性评估场景
本文提出一种新方法，通过引入增量学习技术改进二进制加法树（BAT）算法。BAT因其开发、实现和应用简便，是求解网络可靠性与优化问题的强大隐式枚举方法，但传统上因静态特性难以应对动态和大规模网络。通过增量学习，使BAT能够随着新数据或网络变化持续自适应优化，实现更高效的计算，避免搜索最小路径与割集带来的冗余，显著提升动态环境下的整体性能。实验结果表明，该方法在计算效率和解质量上均显著优于传统BAT及基于路径（MP）和蒙特卡洛（MC）的间接算法。
原文摘要 · Abstract (English)
This paper presents a novel approach to enhance the Binary-Addition-Tree algorithm (BAT) by integrating incremental learning techniques. BAT, known for its simplicity in development, implementation, and application, is a powerful implicit enumeration method for solving network reliability and optimization problems. However, it traditionally struggles with dynamic and large-scale networks due to its static nature. By introducing incremental learning, we enable the BAT to adapt and improve its performance iteratively as it encounters new data or network changes. This integration allows for more efficient computation, reduced redundancy without searching minimal paths and cuts, and improves overall performance in dynamic environments. Experimental results demonstrate the effectiveness of the proposed method, showing significant improvements in both computational efficiency and solution quality compared to the traditional BAT and indirect algorithms, such as MP-based algorithms and MC-based algorithms.
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