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云资源调度使用多策略融合蜂蜜子算法

Haitao Xie¹, Chengkai Li², Zhiwei Ye¹

¹School of Computer Science, Hubei University of Technology, Wuhan, China.

|February 19, 2025

概括

一个改进的蜂蜜子算法 (IHBA) 通过防止本地优化来增强云资源调度. 这种新的方法改善了大数据环境中的任务分配和负载平衡.

科学领域:

计算机科学计算机科学
人工智能的人工智能
大数据分析大数据分析

背景情况:

云资源调度是大数据中的一个关键的组合优化问题.
超启发式算法 (MA) 是常用的,但通常会受到局部最佳的影响,降低分配方案的质量.

研究的目的:

提出一个改进的蜂蜜子算法 (IHBA) 优越的云资源调度.
增强全球搜索能力,减轻安排中的本地最佳问题.

主要方法:

通过整合两个本地搜索策略和一个新的健身功能,开发了IHBA.
在四个不同的负载规模任务中,IHBA与其他六个MA进行了比较.

主要成果:

与其他六个MA相比,IHBA表现优越.
该算法有效地改善了人口多样性,并扩大了搜索范围.
IHBA成功地阻止了局部优化,并实现了高效的资源负载平衡.

结论:

IHBA在云资源调度算法方面取得了重大进展.
拟议的方法为优化大数据资源配置提供了一个强大的解决方案.

关键词:

蜂蜜子的算法蜂蜜子的算法分析和可视化分析和可视化大数据就是大数据.云资源调度云资源调度云资源调度负载平衡系统的负载平衡

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