一个基于竞争的学习的灰狼优化器,用于工程问题,并将其应用于多层感知器训练
Vamsi Krishna Reddy Aala Kalananda1, Venkata Lakshmi Narayana Komanapalli1
1School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014 India.
Multimedia tools and applications
|June 26, 2023
概括
一个新的基于竞争性学习的灰狼优化器 (Clb-GWO) 提高了勘探和开发的平衡. 这种强大的算法在基准测试和训练多层感知子中表现出色,优于现有方法.
科学领域:
- 优化算法 优化算法
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 灰狼优化器 (GWO) 是一个受欢迎的元启发式算法,灵感来自狼群行为.
- 现有的GWO变种经常难以平衡勘探和开发,限制了复杂问题的性能.
- 人口多样性对元启发学至关重要,以避免过早的融合,并增强全球搜索能力.
研究的目的:
- 引入一种基于竞争性学习的新型灰狼优化器 (Clb-GWO),以改善勘探-开采权衡.
- 通过竞争性学习策略和双重搜索系统,增强人口多样性和搜索效率.
- 验证 Clb-GWO 在标准比较功能和现实世界的机器学习任务上的有效性.
主要方法:
- 通过将竞争性学习策略整合到标准GWO框架中,开发了Clb-GWO.
- 纳入人口分为多数和少数群体的细分,采用选择性互补的双重搜索系统.
- 利用差异向量来促进人口多样性,以及在勘探和开采之间取得更好的平衡.
主要成果:
- 与标准GWO及其变体相比,Clb-GWO在CEC2020和CEC2019基准测试套件上表现优异.
- 该算法在训练多层感知器 (MLP) 进行分类和函数近似数据集方面取得了很好的结果.
- 与竞争方法相比,Clb-GWO表现出了统计学上显著的改进,更低的错误率和更低的标准偏差.
结论:
- 拟议的Clb-GWO有效平衡勘探和开采,从而提高了优化性能.
- 竞争式学习方法和人口分类显著提高了人口多样性和搜索能力.
- Clb-GWO被证明是一个强大的和高度竞争的元启发式,用于理论比较和实际机器学习应用.
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