适应性采样步行:一种简单而高效的自主本地搜索.
Matthieu Basseur1, Arnaud Liefooghe2, Sara Tari3
1Univ. Littoral Côte d'Opale, UR 4491, LISIC, F-62100 Calais, France matthieu.basseur@univ-littoral.fr.
Evolutionary computation
|February 10, 2026
概括
我们开发了无参数的本地搜索算法,可以自动调整搜索行为. 这些自主方法在不需要大量调整的情况下,在各种优化问题上取得了强大的结果.
科学领域:
- 人工智能的人工智能
- 运营研究 运营研究
- 计算机科学 计算机科学
背景情况:
- 传统的本地搜索方法通常需要广泛的参数调整以获得最佳性能.
- 开发能够适应问题的特征的自主搜索算法是一个关键的挑战.
研究的目的:
- 引入和探索自动化和自适应性部分社区本地搜索算法.
- 设计具有最小先决条件且没有参数调节的本地搜索方法.
主要方法:
- 扩展采样步行和ID步行算法.
- 在移动窗口上集成基于距离的计算,以动态确定邻里评估大小.
- 开发了无参数的本地搜索技术.
主要成果:
- 在四个基准组合优化问题类上经验评估的无参数方法.
- 与固定参数版本进行性能比较.
- 在不同的解决方案表示,社区结构和健身景观的各种问题中表现出强大而有竞争力的结果.
结论:
- 无参数的本地搜索方法是可行的和有效的.
- 自主本地搜索方法为参数调节算法提供了一种通用且强大的替代方案.
- 提出的方法验证了通用自主本地搜索的潜力.
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