在复杂网络中的组合问题上进行多域进化优化
IEEE transactions on cybernetics
|October 6, 2025
概括
本研究介绍了多域进化优化 (MDEO),这是一个用于跨不同复杂系统的知识转移的新框架. 通过利用共同的特征,MDEO有效地将解决方案转移到各个领域之间,优于传统方法.
科学领域:
- 人工智能的人工智能
- 复杂的系统复杂的系统.
- 优化优化 优化优化
背景情况:
- 多任务进化优化 (MTEO) 专注于知识转移的任务相似性.
- 现实世界的复杂系统往往具有共同的基本特征 (例如,权力法,社区结构).
- 在不同领域中利用这些共享特征,为优化提供了尚未开发的潜力.
研究的目的:
- 引入一个新的框架,多领域进化优化 (MDEO),用于跨多个领域的知识转移.
- 开发用于管理和有效地在不同复杂系统之间转移解决方案的机制.
- 通过利用复杂系统的共同特征来增强进化优化.
主要方法:
- 建议社区一级测量图形相似性,以指导知识转移.
- 开发了一个基于图形学习的网络对齐模型,以实现有效的解决方案传输.
- 设计了一个自适应机制来确定转移的解决方案的数量.
- 引入了一种以知识为导向的突变机制,用于适应性地利用转移的知识.
主要成果:
- 拟议的多域进化优化 (MDEO) 框架显示出卓越的有效性.
- 使用不同领域的真实世界网络进行对抗链路扰动的实验验证了框架的性能.
- MDEO的性能优于经典的进化优化方法.
结论:
- 多域进化优化 (MDEO) 有效地利用不同域的共享特征进行增强的优化.
- 拟议的框架为复杂系统中的知识转移提供了一个强有力的方法.
- 这项研究为各种科学领域的跨领域优化开辟了新的途径.
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