在因子化机器中高效的比特标签,用于旅行销售员的回火问题
Shota Koshikawa1, Aruto Hosaka2, Tsuyoshi Yoshida2
1Information Technology R&D Center, Mitsubishi Electric Corporation, Kanagawa, 247-8501, Japan. Koshikawa.Shota@ds.MitsubishiElectric.co.jp.
Scientific reports
|July 24, 2025
概括
本研究探讨了用于优化问题的二进制标签方法. 灰色标签通过减少局部最小值来提高旅行销售员问题 (TSP) 的收速度和准确性.
科学领域:
- 计算数学 计算数学 计算数学
- 机器学习 机器学习
- 运营研究 运营研究
背景情况:
- 有效地解决大规模优化问题需要将参数转换为机器可读的变量.
- 二次式不受约束的二进制优化 (QUBO) 是一个关键的技术,通常采用机器学习模型,如带的因子化机器.
- 选择二进制标签方法显著影响成本函数形状和对局部最小值的易感性.
研究的目的:
- 调查不同的二进制标签方法如何影响优化算法的融合速度和准确性.
- 为了评估一种新的标签策略 - - 灰色标签 - - 在解决优化问题的表现.
主要方法:
- 该研究的重点是二次式不受约束的二进制优化 (QUBO) 问题.
- 使用旅行销售员问题 (TSP) 作为基准,进行了数值模拟.
- 提出了灰色标签,并与二进制变量转换的自然标签进行了比较.
主要成果:
- 与自然标签相比,灰色标签显示了局部最小值的百分比降低.
- 拟议的灰色标签导致模拟的TSP实例的旅行距离缩短.
- 性能在有限数量的代中进行了评估,突出了效率的提高.
结论:
- 二进制标签方法极大地影响了QUBO解决器的性能.
- 灰色标签为TSP提供了一种优越的方法,并通过最小化局部优化来解决潜在的其他优化问题.
- 这项研究为增强基于机器学习的优化策略提供了有价值的方法.
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