一个基于ResNet18的拉格朗奇互波运算符的新型黑寡妇优化算法
Peiyang Wei1,2,3,4,5, Can Hu2, Jingyi Hu2
1School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Biomimetics (Basel, Switzerland)
|June 25, 2025
概括
这项研究介绍了LIBWONN,一种进化算法,可以优化神经网络的学习速度. 利博恩 (LIBWONN) 显示出卓越的收性和稳定性,提高了对不同数据集的模型准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 超参数显著影响神经网络的训练和性能.
- 优化学习速率至关重要,但由于任务/数据集依赖性和试错方法,具有挑战性.
- 进化计算为提高效率提供了自动化的超参数调整.
研究的目的:
- 提出一种新的算法,LIBWONN (基于拉格朗日插值的黑寡妇优化算法),用于优化神经网络学习速率.
- 提高ResNet18模型的培训效率和性能.
- 为了解决手动学习速度调节的复杂性和耗时性质.
主要方法:
- 开发了LIBWONN,将拉格朗日插值与黑寡妇优化算法集成在一起.
- 根据CEC2017和CEC2022的24个基准函数对LIBWONN进行了评估.
- 将LIBWONN与9个先进的元启发算法进行比较.
- 使用六个不同的,公开可用的数据集在ResNet18上测试了LIBWONN的性能.
主要成果:
- 与其他九个基准函数的元启发算法相比,LIBWONN表现出优越的融合和稳定性.
- 在ResNet18的训练 (6.99%) 和测试 (4.48%) 套件上,LIBWONN实现了显著的准确性改进.
- 拟议的算法性能优于标准的黑寡妇优化 (BWO) 算法.
结论:
- LIBWONN有效地优化了ResNet18的学习速度,超过了现有的元启发方法.
- 整合拉格朗奇插值可以提高黑寡妇优化算法的性能.
- 利博恩 (LIBWONN) 提供了一个有前途的自动化解决方案,用于改进神经网络的训练和在各种应用程序中的泛化.
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