鱼优化用于云端卸载智能电网服务的决策
Gabriel Ioan Arcas1, Tudor Cioara2, Ionut Anghel2
1Bosch Engineering Center, 400158 Cluj-Napoca, Romania.
Biomimetics (Basel, Switzerland)
|May 24, 2024
概括
本研究介绍了一种鱼优化算法,用于选择智能能源网的最佳边缘节点,改善数据传输和控制服务延迟. 该方法有效地管理计算任务,提高决策速度和能源网络安全.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 优化算法 优化算法
背景情况:
- 智能电网中越来越多的物联网设备产生大量数据,挑战控制服务延迟和安全的能源供应.
- 边缘计算通过卸载计算提供了一个解决方案,但由于巨大的决策空间,协调边缘节点是复杂的.
研究的目的:
- 开发一种优化的方法来选择边缘节点,用于智能电网中的计算任务卸载.
- 解决智能能源网中的延迟,数据量和安全能源交付方面的挑战.
主要方法:
- 利用鱼优化算法 (WOA) 进行最佳边缘节点选择.
- 采用定向非循环图 (DAG) 来建模依赖关系并导航决策空间.
- 开发了一个健身功能,考虑往返时间和边缘任务资源相关性,用于卸载决策.
- 适应WOA,修改反机制,惯性重量和趋同因子,以防止低于最佳的解决方案.
主要成果:
- 拟议的解决方案有效地平衡了能源和数据网络的限制.
- 证明了更快的决策优化,响应时间显著改善.
- 实现了低平均执行时间,每次代约为0.03秒.
- 在复杂的基础设施上展示了在多样性,健身进化和执行时间方面的强表现.
结论:
- 鱼优化算法为智能电网中的边缘节点选择提供了一种有效的方法.
- 该方法提高了优化速度和资源管理,同时考虑了网络约束.
- 为提高智能能源网的效率和可靠性提供了强大的解决方案.
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