一个多策略的西伯利亚老虎优化算法用于任务安排在远程传感数据批处理处理
Ziqi Liu1, Yong Xue2, Jiaqi Zhao1
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
Biomimetics (Basel, Switzerland)
|November 26, 2024
概括
一个新的多策略改进的西伯利亚虎优化 (MSSTO) 算法提高了遥感数据处理效率. 它显著减少了任务完成时间,并优化了资源配置,以提高计算性能.
科学领域:
- 地球和太空科学 地球和太空科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 遥感数据的指数增长压倒了传统的计算模型.
- 对庞大的地理空间数据集的高效处理对于全球观测至关重要.
- 分布式集群计算任务调度影响完成时间和资源利用.
研究的目的:
- 为了提高远程传感数据的任务处理效率.
- 在分布式环境中优化计算资源的分配.
- 为任务调度开发一个改进的优化算法.
主要方法:
- 开发多策略改进的西伯利亚老虎优化 (MSSTO) 算法.
- 帐混沌地图,莱维飞行,考西突变和学习策略的整合.
- 应用随机密钥和统一分配编码方案用于任务调度.
主要成果:
- MSSTO算法展示了卓越的融合速度和全球最佳解决方案搜索.
- 与原来的STO算法相比,任务完成时间减少了21%.
- 与九个先进的算法相比,完成时间平均减少了15%.
结论:
- MSSTO算法显著提高了任务处理效率和资源分配.
- 拟议的方法为任务安排提供了卓越的解决方案准确性和融合速度.
- 在气溶光学深度检索方面实现了最佳的执行序列和机器分配方案.
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