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Updated: Jun 27, 2025

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基于生物地理学的适应性息地优化器,用于优化图像分类中的深度CNN超参数.

Jiayun Xin1, Mohammad Khishe2,3, Diyar Qader Zeebaree4

  • 1School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, Shandong, China.

Heliyon
|May 1, 2024
PubMed
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此摘要是机器生成的。

本研究介绍了基于生物地理学的适应性息地优化器 (AHBBO),用于调整深度卷积神经网络 (DCNNs) 以进行图像分类. AHBBO增强了探索和多样性,大大提高了DCNN的性能,降低了错误率.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 计算机视觉 计算机视觉

背景情况:

  • 深度卷积神经网络 (DCNN) 在图像分类方面表现出色,但面临着超参数优化挑战.
  • 传统的基于生物地理的优化 (BBO) 可能会在复杂的任务中遭受过早的融合和有限的探索.

研究的目的:

  • 开发一个改进的优化算法,即适应性息地生物地理基础优化器 (AHBBO),用于调整DCNN的超参数.
  • 增强BBO的勘探能力和人口多样性,以获得更好的优化性能.

主要方法:

  • 基于生物地理学的适应性息地优化器 (AHBBO) 是用可变息地大小和受调节的突变开发的.
  • 在53个基准优化函数上对AHBBO进行了评估.
  • 在9个不同的图像分类数据集上,DCNN-AHBBO模型与23个已建立的图像分类器进行了基准测试.

主要成果:

  • AHBBO展示了改进的初始解决方案,更快的融合,以及对基准函数的卓越性能.
  • 与现有分类器相比,DCNN-AHBBO模型实现了高达5.14%的错误率显著降低.
  • 在95个评估中,AHBBO在87个评估中超过了13个基准分类器,展示了它的有效性.
关键词:
适应性息地适应性息地基于生物地理学的优化器.深度卷积神经网络是一种深度卷积神经网络.数字图像分类数字图像分类

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结论:

  • 拟议的AHBBO算法提供了一种高性能和可靠的方法,用于优化图像分类中的深度神经网络 (DNN).
  • 这项研究通过提供有效的优化技术来提高DCNN效率来推进深度学习.