对基于GAN的不平衡分类Ivy算法的协同增强
Hanjie Xu1, Jian Xiong1, Jinyu Wu2
1Guangzhou Xinhua University, Guangzhou, 510520, China.
Scientific reports
|December 21, 2025
概括
增强的常春藤算法 (E-IVYA) 通过平衡勘探和开发,提高了对复杂的优化任务的群体智能. 它在基准指标上取得了卓越的表现,并显著提高了用于不平衡数据分类的生成对抗网络超参数优化.
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
- 优化技术 优化技术
背景情况:
- 标准的常春藤算法 (IVYA) 是一种群智能优化方法,灵感来自植物生长.
- 在高维度问题上,平衡勘探和开发对于IVYA的效率至关重要.
- 现有的方法在维持多样性和逃避局部最佳状态方面面临挑战.
研究的目的:
- 提出一个增强的常春藤算法 (E-IVYA),以提高优化性能.
- 为了解决在群体智能中探索-开发平衡的挑战.
- 提高自动机器学习的效率,特别是超参数优化.
主要方法:
- 引入了人口多样性的动态扰动框架 (对称和不对称的探索).
- 实施了使用精英差异突变来避免停滞的动态逃生机制.
- 集成了一个灵感来自于Sine-Cosine算法的适应性移动策略,用于勘探-开发平衡.
主要成果:
- 在IEEE CEC 2014和2017测试套件上,E-IVYA与基准算法相比表现优越.
- 该算法在信用卡欺诈数据集上获得了0.87的高F1-Score,用于少数群体的分类.
- 对于不平衡数据,E-IVYA在优化生成对抗网络方面明显优于SMOTE等标准技术.
结论:
- 增强的常春藤算法 (E-IVYA) 是一个强大的和高效的优化工具.
- E-IVYA有效地解决了复杂的,高维度的优化问题.
- 提议的改进为自动机器学习任务提供了一种优越的方法,特别是在不平衡的数据集中.
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