增强加权K-平均灰狼优化器:用于数据聚类问题的增强的元启发算法
Manoharan Premkumar1, Garima Sinha2, Manjula Devi Ramasamy3
1Department of Electrical & Electronics Engineering, Dayananda Sagar College of Engineering, Kumaraswamy Layout, Bengaluru, Karnataka, 560078, India. mprem.me@gmail.com.
Scientific reports
|March 5, 2024
概括
本研究介绍了使用K-means集群进行更好的数据集群的增强灰狼优化器. 新的算法显著改善了找到最佳集群的方法,并避免过早的融合,优于标准版本的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 优化算法 优化算法
背景情况:
- 数据聚类对于将信息组织成有意义的组至关重要.
- 传统的灰狼优化器 (GWO) 在探索和利用有效集群方面扎.
- 过早的融合限制了标准的元启发算法的性能.
研究的目的:
- 增强灰狼优化器 (GWO) 以提高数据聚类性能.
- 解决GWO在勘探和开采能力方面的局限性.
- 为了引入一种新的算法,K-means基于集群的GWO,用于优化优化.
主要方法:
- 整合K-means算法概念以改进初始解决方案.
- 包括一个新的重量因子来增强解决方案的多样性.
- 使用基于分区集群的健身功能进行评估.
主要成果:
- 基于K-means集群的GWO表现出比标准GWO更高的性能.
- 在数值和数据聚类任务中实现了大约34%的性能改善.
- 在各种数据集中高效地生产高质量的集群中心.
结论:
- 基于K-means集群的GWO是一种强大的,可靠的数据集群方法.
- 与传统的元启发式集群技术相比,它是一个显著的进步.
- 建立了一个新的基准,用于未来的研究在metaheuristic集群算法.
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