结合贪和进化算法,在确定性线性值模型下最大限度地影响网络
Alexander Andreev1, Stepan Kochemazov2, Alexander Semenov1
1Information Technologies and Programming Faculty, ITMO University, Saint Petersburg, Russia.
PloS one
|September 8, 2025
概括
本研究介绍了根据确定性线性值模型 (DLTM) 在布尔网络中影响最大化 (IM) 和目标组选择 (TSS) 的新进化算法. 这些混合算法在大规模网络上显著优于现有的方法.
科学领域:
- 计算科学 计算科学
- 网络科学 网络科学
- 优化优化 优化优化
背景情况:
- 影响最大化 (IM) 和目标组选择 (TSS) 是网络分析中的关键问题.
- 在确定性线性值模型 (DLTM) 下,IM和TSS的现有方法具有局限性.
- 布尔网络被广泛用于模拟复杂系统.
研究的目的:
- 为了在伪布尔优化框架内对布尔网络进行IM和TSS问题进行重构.
- 开发和评估新的混合算法,将进化计算与贪的启发式计算相结合,以解决这些问题.
- 提出一个专门的 (1+1) 进化算法变体,优化了布尔超立方体的固定哈明重量子集.
主要方法:
- 影响力最大化和目标集选择的制定作为伪布尔优化问题.
- 开发一种新的 (1+1) 进化算法变体,用于优化固定哈明重量的布尔超立方体上的函数.
- 拟议的进化算法的混合化,用一个贪的启发式来初始化IM和TSS中的解决方案.
主要成果:
- 拟议的混合算法显示显著优越的性能相比,贪的启发式的组合与经典的 (1+1) 进化算法.
- 在现实世界和随机网络上的实验验证表明了新算法的有效性.
- 这些算法是可扩展的,适用于拥有数万个顶点的大型网络.
结论:
- 新的混合进化算法为在确定性线性值模型下解决影响最大化和目标组选择问题提供了更有效的方法.
- 专门的 (1+1) 进化算法变体非常适合影响最大化任务.
- 开发的方法为分析大规模布尔网络提供了强大的计算工具.
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