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优化VGG16深度学习模型与增强的饥饿游戏搜索标志分类.
Mohammed Hussain1, Thaer Thaher2, Mohamed Basel Almourad1
1College of Technological Innovation, Zayed University, Dubai, United Arab Emirates.
Scientific reports
|December 31, 2024
概括
本研究引入了一个增强的饥饿游戏搜索 (EHGS) 算法,以优化VGG16超参数用于标志分类. EHGS-VGG16模型实现了卓越的准确性,证明了在图像识别任务的深度学习中进化优化的力量.
科学领域:
- 计算机视觉和机器学习
- 群体智能和优化算法 群体智能和优化算法
- 深度学习架构用于图像识别.
背景情况:
- 准确的标志分类至关重要,但由于大小,定向和背景复杂性的变化,具有挑战性.
- 像VGG16这样的深度学习模型是有前途的,但需要广泛的超参数调整.
- 现有的群体智能算法,包括饥饿游戏搜索 (HGS),面临着限制,例如受限的人口多样性和局部最佳.
研究的目的:
- 提出一个优化的深度学习架构,EHGS-VGG16,通过增强饥饿游戏搜索 (HGS) 算法进行超参数调整.
- 通过修改搜索策略来提高HGS的勘探能力,包括"本地最佳"和"本地逃生机制".
- 评估拟议的EHGS-VGG16标志分类模型的有效性.
主要方法:
- 开发了一个增强的饥饿游戏搜索 (EHGS) 算法,具有改进的探索能力.
- 在IEEE CEC2014套件中的30个实值基准函数上评估了EHGS算法.
- 在Flickr-27标识分类数据集上实现并测试了VGG16模型,并将其与其他最先进的深度学习模型进行比较. 集成的EHGS用于超参数优化.
主要成果:
- VGG16模型实现了高性能,在Flickr-27数据集上表现优于ResNet50V2,InceptionV3,DenseNet121,EfficientNetB0和MobileNetV2等.
- 拟议的EHGS算法在基准功能评估中表现得更好.
- 将EHGS与VGG16集成用于超参数调整,导致标识分类准确度进一步提高3%.
结论:
- 与标准VGG16和其他深度学习模型相比,EHGS-VGG16模型显著提高了标志分类的准确性.
- 增强的饥饿游戏搜索算法有效地解决了标准HGS的局限性,改善了探索,避免了局部最佳.
- 将进化优化技术与深度学习相结合,为提高像标识分类等复杂图像识别任务的准确性提供了有希望的方法.
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