一种混合进化算法,用于在复杂网络中最大化影响,使用侵入性杂草优化和引力搜索.
Linian Liu1, Junyi Xu2, Shouliang Lai3
1Department of Multimedia Design, Graduate School of Design, Hanyang University, Seoul, 04763, Korea.
Scientific reports
|November 11, 2025
概括
我们开发了一种混合杂草引力进化算法 (HWGEA),用于复杂的优化任务. 这种新的算法在连续和离散优化中提高了性能,包括社交网络影响力最大化.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 社交网络对于信息传播至关重要,但优化它们庞大的搜索空间是困难的.
- 现有的优化方法与大规模问题的崎地貌作斗争.
研究的目的:
- 引入一种新的混合杂草引力进化算法 (HWGEA),以实现强大的持续优化.
- 开发一个离散的变体 (DHWGEA) 以在社交网络中有效地最大化影响力.
- 在优化中增强勘探和开采之间的平衡.
主要方法:
- HWGEA将侵入性杂草优化和重力搜索与适应性突变结合在一起.
- DHWGEA结合了拓意识初始化,动态本地搜索和预期影响力得分替代品.
- 算法在23个连续基准和现实世界的工程设计上进行了测试.
主要成果:
- 在连续基准指标上,HWGEA取得了卓越的表现,超过了几种已建立的算法.
- 在工程设计问题上,HWGEA表现出了具有竞争力的结果.
- DHWGEA为影响力最大化提供了一个实际的准确性-效率权衡,超过了PageRank.
结论:
- HWGEA和DHWGEA提供了一个连贯的,可扩展的框架,用于连续和离散优化.
- 算法的自适应组件减少了手动参数调整的需要.
- 这些算法为计算机科学和人工智能的复杂优化挑战提供了强大而高效的方法.
关键词:
适应性突变是一种适应性突变.复杂的网络优化复杂的网络优化离散的进化算法 离散的进化算法引力搜索是引力搜索的一个方法.这是一种混合的元启发式传闻.影响力最大化影响力最大化侵袭性杂草优化 侵袭性杂草优化更多相关视频
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