增强的aquila优化器用于全球优化和数据聚类
Laith Abualigah1, Saleh Ali Alomari2, Mohammad H Almomani3
1Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan. aligah.2020@gmail.com.
Scientific reports
|April 16, 2025
概括
基于局部对立的学习Aquila优化器 (LOBLAO) 增强了全球优化和数据聚类. 这种修改后的算法通过克服局部最佳值和过早的收来提高高维问题上的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 阿奎拉优化器 (AO) 是一种由阿奎拉鸟行为启发的元启发算法.
- AO在高维优化方面表现出局限性,包括狭窄的探索和过早的局部最佳趋同.
研究的目的:
- 引入基于局部对立的学习Aquila优化器 (LOBLAO),这是AO的一个新型变体.
- 解决 AO 在高维优化的局限性,提高全球优化和数据集群的性能.
主要方法:
- 整合基于对立的学习 (OBL) 以增强解决方案多样性和平衡探索/利用.
- 整合突变搜索策略 (MSS) 以减轻局部最佳情况并确保强大的搜索空间探索.
主要成果:
- 与原来的AO和其他最先进的算法相比,LOBLAO在基准函数和数据集群任务上表现出更高的性能.
- 在集群问题中,LOBLAO平均获得了1.625的排名,这表明了高强度和多功能性.
- 该算法有效地处理了高维数据集,超过了现有方法.
结论:
- LOBLAO显著改进了原来的AO,特别是在高维优化问题上.
- 提议的改进 (OBL和MSS) 有效地解决了过早的融合和局部最佳问题.
- LOBLAO为研究和实践中的多样化和具有挑战性的优化任务提供了一个强大而通用的工具.
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