基于图像分类的两个进化计算的混合算法
Peiyang Wei1,2,3,4,5,6, Rundong Zou2, Jianhong Gan2,4,6
1School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
本研究介绍了一种混合优化算法 (HGAO),用于增强DenseNet-121的图像分类. 该算法有效地优化了超参数,提高了分类准确性和模型稳定性.
科学领域:
- 计算机科学
- 人工智能
- 机器学习
背景情况:
- 卷积神经网络 (CNN),包括像DenseNet-121这样的先进模型,在图像分类方面表现出色,但在超参数优化和梯度稳定方面面临挑战.
- 进化算法提供了潜在的解决方案,因为它们的探索和利用优势,解决了当前CNN模型的局限性.
研究的目的:
- 通过使用新型混合进化算法优化DenseNet-121超参数来提高图像分类性能.
- 提高分类准确性和模型稳定性,同时减轻梯度消失和爆炸等问题.
主要方法:
- 一种混合算法 (HGAO) 结合了角算法与二次插入和巨优化与牛顿插入.
- 为了优化DenseNet-121模型的关键超参数,特别是学习率和学率,使用了HGAO算法.
- 优化的DenseNet-121模型在五个不同的图像数据集上进行了评估,与使用精度,精度,回忆和F1得分指标的九个最先进的算法进行了性能比较.
主要成果:
- 使用HGAO的超参数优化导致了更有效的参数组合,从而显著提高了性能.
- 在训练组中,准确度增加了0.5%,损失减少了0.018.
- 在试验组中,准确度提高了0.5%,损失减少了54个点,证明了更好的分类性能和稳定性.
结论:
- HGAO算法提供了一种优化DenseNet-121超参数的有效方法,提高了分类准确性和模型稳定性.
- 提出的方法成功地解决了梯度困难,并提高了对图像分类中的深度学习模型的超参数优化的整体有效性.
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