一个超变压器模型用于可控制的帕雷托前线学习,具有分割可行性约束.
Tran Anh Tuan1, Nguyen Viet Dung1, Tran Ngoc Thang1
1Faculty of Mathematics and Informatics, Hanoi University of Science and Technology; Center for Digital Technology and Economy (BK Fintech), Hanoi University of Science and Technology, Hanoi, Vietnam.
概括
本研究介绍了一种新的超变压器模型,用于可控帕雷托前端学习与分割可行性约束,提高多目标优化问题的准确性.
科学领域:
- 优化优化 优化优化
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 可控制的帕雷托前线学习 (CPFL) 接近帕雷托最佳解决方案.
- 实际应用往往涉及特定约束区域内的决策者目标.
- 之前的CPFL模型使用了超级网络多层感知器 (Hyper-MLP) 架构.
研究的目的:
- 为 CPFL 开发一个先进的模型,使用分割可行性约束 (SFC).
- 利用变压器架构的优势,在受约束的多目标优化中提高性能.
- 为了提高接近帕雷托最佳解决方案的准确性,在可行性限制下设置最佳解决方案.
主要方法:
- 开发一种新的超变压器 (Hyper-Trans) 模型,用于CPFL与SFC.
- 在决策空间的定义约束区域内专门训练模型.
- 使用序列对序列的普遍近似理论进行模型验证.
主要成果:
- 与Hyper-MLP模型相比,提出的Hyper-Trans模型显示了减少的平均平方误差 (MED).
- 超级传输模型有效地处理了具有分割可行性约束的多目标优化问题.
- 计算实验证实了Hyper-Trans架构的卓越性能.
结论:
- 超变压器模型为可控制的帕雷托前线学习提供了更准确和更有效的方法,具有分割可行性约束.
- 这一进步使复杂的多目标优化问题具有实际约束的更好的解决方案成为可能.
- 这些发现表明变压器是高级优化任务的有希望的架构.
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