改进的殖民地捕食算法优化卷积神经网络用于心电图信号分类
Xinxin He1, Weifeng Shan1, Ruilei Zhang1
1School of Emergency Management, Institute of Disaster Prevention, Sanhe 065201, China.
Biomimetics (Basel, Switzerland)
|July 28, 2023
概括
这项研究引入了一种改进的殖民地掠夺算法 (CPA),称为OLCPA,增强了全球搜索能力. OLCPA-CNN模型有效地以高准确度对医疗数据集进行分类.
科学领域:
- 计算智能是一种计算智能.
- 群体情报算法 群体情报算法
- 机器学习应用程序 机器学习应用程序
背景情况:
- 群体智能算法为复杂的现实世界问题提供了灵活性.
- 殖民地捕食算法 (CPA) 是一种以自然为灵感的算法,在探索和逃离当地的最佳状态方面存在局限性.
- 提高全球搜索能力对于群集智能算法至关重要.
研究的目的:
- 提高殖民地掠食算法 (CPA) 的全球搜索能力.
- 提出一种新的OLCPA-CNN模型,用于对卷积神经网络 (CNN) 的参数调整.
- 评估改进的算法和对基准函数和现实世界数据集的拟议模型的性能.
主要方法:
- 一个改进的变体,正交学习殖民地捕食算法 (OLCPA),是通过结合正交学习策略来开发的.
- 提出了一个新的OLCPA-CNN模型,利用OLCPA优化CNN参数.
- 使用IEEE CEC 2017基准函数和医疗数据集 (MIT-BIH心律失常,欧洲ST-T) 进行了比较实验.
主要成果:
- OLCPA算法表现出卓越的性能,与其他传统和先进的算法相比,在基准函数上排名第一.
- 在MIT-BIH心律失常和欧洲ST-T数据集上,OLCPA-CNN模型分别实现了97.7%和97.8%的高分类精度.
- 拟议的OLCPA有效地解决了CPA的局限性,特别是在逃避局部最佳状态方面.
结论:
- 正交学习殖民地捕食算法 (OLCPA) 显著提高了原CPA的全球搜索能力.
- OLCPA-CNN模型为准确的医疗数据分类提供了一个强大的方法.
- 这项研究有助于推动群集智能算法及其在机器学习中的实际应用.
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