多种人口黑洞算法用于数据聚类问题
Sinan Q Salih1, AbdulRahman A Alsewari2, H A Wahab3
1Technical College of Engineering, Al-Bayan University, Baghdad, Iraq.
PloS one
|July 5, 2023
概括
一个新的多种群黑洞算法 (MBHA) 通过改进解决方案探索和融合来增强数据聚类. 这种以自然为灵感的方法为复杂的数据挖掘任务提供了精确而强大的结果.
科学领域:
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 数据聚类 (DC) 对于信息检索至关重要,它将类似的数据点分组在一起.
- 传统的集群方法面临挑战,需要先进的优化技术.
- 黑洞算法 (Black Hole Algorithm,简称BHA) 是一种以自然为灵感的,用于优化问题的元启发方法.
研究的目的:
- 解决原来的黑洞算法 (BHA) 的局限性,特别是其探索能力.
- 为了提高性能,引入BHA (MBHA) 的通用化,多人群版本.
- 评估MBHA在数据聚类 (DC) 任务中的有效性.
主要方法:
- 开发了一个多种群黑洞算法 (MBHA),专注于一组最佳解决方案,而不是单一的最佳解决方案.
- 在九个基准测试函数上测试了MBHA,以评估其精度和稳定性.
- 将MBHA应用于来自UCL机器学习实验室的六个现实世界数据集,用于数据集群评估.
主要成果:
- 与原始BHA和其他对基准函数的算法相比,MBHA表现出非常精确的结果和出色的稳定性.
- 在真实世界数据集上实现了高合率,表明适合数据聚类.
- 实验结果证实了MBHA在解决数据聚类问题的优越性.
结论:
- 拟议的多种群黑洞算法 (MBHA) 是一种强大而有效的优化技术.
- MBHA显著改进了原来的BHA,提供了更好的探索和融合.
- 该算法非常适合解决机器学习中的复杂数据聚类挑战.
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