模糊的自适应多任务优化
IEEE transactions on cybernetics
|October 9, 2025
概括
本研究介绍了用于同时优化的模糊自适应多任务优化 (FAMTO). FAMTO增强了知识转移和搜索运营商的性能,在基准和现实世界的应用上表现优于现有的方法.
科学领域:
- 人工智能的人工智能
- 计算智能是一种计算智能.
- 优化优化 优化优化
背景情况:
- 进化式多任务优化 (EMTO) 旨在同时优化多个任务.
- 现有的EMTO算法经常使用固定的知识传输概率和单个搜索运算符,限制了适应性.
- 模糊系统为复杂,相互依存的问题提供适应性,如EMTO.
研究的目的:
- 提出一个模糊的自适应多任务优化 (FAMTO) 框架.
- 为知识转移和进化搜索运营商开发适应性策略.
- 为了提高EMTO的性能和适用性.
主要方法:
- 实施了模糊适应性转移 (FAT) 策略,以根据后代的生存和质量进行适应性知识转移概率调整.
- 采用模糊逻辑来管理相互依赖的指标,以实现可靠的转移概率调整.
- 引入了基于个体的随机选择 (IRS) 策略,用于自适应的任务内进化搜索操作员选择.
主要成果:
- 与最先进的EMTO算法相比,FAMTO在CEC17和CEC22基准上表现明显优越.
- 拟议的方法在应用于现实世界平面动力学臂控制问题时显示出实际适用性.
- 对多任务优化问题 (MaTOPs) 的扩展实验证实了FAMTO的可扩展性.
结论:
- 在传统的EMTO中,FAMTO有效地解决了固定参数的限制.
- 适应性策略可以提高各种任务的优化性能和稳定性.
- FAMTO为复杂的多任务优化挑战提供了一个可扩展和适用的解决方案.
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