多策略增强的人工子优化,用于预测旅游服务沟通课程中的成绩
Xiaodan Qu1, Zhuyin Jia2,3
1Zhejiang Industry & Trade Vocational College, Wenzhou, 325005, China.
Scientific reports
|July 4, 2025
概括
一个新的多策略增强的人工子优化算法 (MEARO) 提高了学生成绩预测的准确性. MEARO提高了优化,导致更好的分类性能和确定影响学生成功的关键因素.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 教育数据挖掘教育数据挖掘
背景情况:
- 预测学生的学业成绩在教育中至关重要.
- 人工智能为提高预测准确性提供了有希望的解决方案.
- 现有的元启发算法面临着局部最佳和缓慢融合等挑战.
研究的目的:
- 引入一个新的优化算法,MEARO,以改善学生的成绩预测.
- 在优化中增强勘探-开采平衡和人口多样性.
- 为了优化Kernel极端学习机器 (KELM) 的参数,以便进行高级分类.
主要方法:
- 开发了多策略增强的人工子优化算法 (MEARO).
- 综合非线性探索和利用过渡因子 (NL) 和随机中心点倒向学习 (SOBL).
- 应用动态变化步骤长度开发策略和与CEC2017算法进行基准测试.
主要成果:
- 在CEC2017基准上,MEARO在与八个复杂的算法对抗时表现出卓越的表现.
- 在优化其参数时,MEARO显著提高了KELM分类性能.
- 优化的KELM模型在学生绩效数据集上超过了其他基准模型.
结论:
- MEARO有效地解决了标准元启发算法的局限性.
- 使用MEARO优化KELM为学生绩效预测提供了一个强大的工具.
- 课程兴趣,讨论频率和获取补充信息是关键的绩效驱动因素.
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