α 在无限范围的Ising模型中对群优化的化
Shintaro Mori1, Taiyo Shimizu1, Masato Hisakado2
1Hirosaki University, Graduate School of Science and Technology, Bunkyo-cho 3, Hirosaki, Aomori 036-8561, Japan.
Physical review. E
|February 7, 2025
概括
群优化使用参数α进行增强搜索. 线性系统中的α-化有助于在复杂问题中找到最佳解决方案,类似于量子化.
科学领域:
- 计算智能是一种计算智能.
- 统计物理学的统计物理.
- 优化算法优化算法
背景情况:
- 群优化 (ACO) 使用参数α来平衡解决方案的探索和利用.
- 在复杂的场景中,最佳α值和回火时间表具有挑战性.
- 本研究侧重于无限范围的伊辛格模型中的线性系统中的α-化过程.
研究的目的:
- 研究α-annealing过程,以应对ACO参数调节中的挑战.
- 分析系统地增加α对找到基本状态的影响.
- 探索α和量子之间的类比.
主要方法:
- 在线性系统中对α化过程的系统研究.
- 对于费洛蒙比率的福克-普朗克方程的导数.
- 在静态状态下计算联合概率密度函数 (PDF).
主要成果:
- 随着α的增加,观察到从单模向多模联合PDF的过渡.
- 证明α-annealing在Ising模型中促进从微不足道的解决方案到基本状态的过渡.
- 建立了参数α与量子化中的横向场之间的类比.
结论:
- α-annealing 在导航复杂的优化问题时是有效的.
- 这些发现表明α-annealing在无限范围的Ising模型之外具有更广泛的适用性.
- 这种方法为增强优化策略提供了一个有前途的方法.
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