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Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
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适应性混沌高斯镜头蛇优化算法,用于改善棉花田传感器覆盖率和利用率.

Chenglong Ban1, Xiang Liu1, Min Tian2

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, China.

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本研究介绍了一种自适应混沌高斯透镜蛇优化算法 (ACGLSOA),用于改善棉花田中的土壤温度无线传感器网络 (STWSNs). 与现有方法相比,ACGLSOA显著提高了网络覆盖范围和节点效率.

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科学领域:

  • 农业工程 农业工程
  • 计算机科学 计算机科学
  • 优化算法 优化算法

背景情况:

  • 土壤温度无线传感器网络 (STWSNs) 对精密农业至关重要,特别是在棉花种植中.
  • 现有的启发式算法努力最大限度地覆盖,同时最大限度地减少STWSN中的传感器节点.
  • 需要先进的优化技术来提高STWSN部署效率.

研究的目的:

  • 引入适应性混沌高斯镜头蛇优化算法 (ACGLSOA) 以优化STWSN部署.
  • 提高农业传感器网络的网络覆盖率和节点利用效率.
  • 为了克服STWSN设计中当前启发式算法的局限性.

主要方法:

  • 通过整合当地搜索和混沌运营商的新型适应因素来改进解决方案,开发了ACGLSOA.
  • 采用了改进的高斯运算机和镜头反射机制,以扩大搜索空间,以获得更好的全球性能.
  • 评估了ACGLSOA与SO,ABC,RIME和PSO等既定算法的性能.

主要成果:

  • 在STWSNs方面,ACGLSOA实现了98.91%的网络覆盖率和73.8%的节点利用效率.
  • 在覆盖范围和节点利用效率方面,与SO,ABC,RIME和PSO相比显著改善.
  • 与比较算法相比,ACGLSOA提供了高达29.68%的覆盖率改进和高达22.13%的效率提升.

结论:

  • ACGLSOA为部署STWSNs提供了一种优越的方法,通过高效的节点使用实现高覆盖率.
  • 拟议的算法有效地解决了优化农业传感器网络放置现有方法的局限性.
  • 在对农业无线传感器网络应用优化技术方面,ACGLSOA 是一个重大进展.