无线传感器网络的覆盖范围优化方法,使用集群智能优化
Shuxin Wang1, Qingchen Zhang2, Yejun Zheng3
1School of Intelligent Manufacturing, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China.
Biomimetics (Basel, Switzerland)
|November 26, 2025
概括
这项研究增强了Flamingo搜索优化算法 (FSA),以改善无线传感器网络 (WSN) 的覆盖范围. 优化的FSA克服了局部优势并加快了融合,从而实现了显著更好的节点部署和网络覆盖.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 无线传感器网络 (WSN) 面临着覆盖范围优化挑战,包括传统算法中的局部优化和动态场景中的缓慢融合.
- 由于节点能量消耗不平衡,现有的方法与"覆盖孔"和实时维护高覆盖率作斗争.
研究的目的:
- 增强Flamingo搜索优化算法 (FSA) 以改善WSN覆盖范围的优化.
- 解决传统算法的局限性,例如对局部最佳的易受性和动态WSN环境中的缓慢融合.
主要方法:
- 集成基于精英反对派的学习策略和阶段性阶段性大小控制到FSA.
- 引入一个代数变化因子,并按阶段控制阶段大小,以便在以后的代过程中逃避局部最佳值.
- 应用改进的FSA来优化传感节点部署,使用覆盖率作为适应性函数和混乱序列进行初始化.
主要成果:
- 与原来的FSA相比,改进的FSA显示覆盖率增加了7.48% (100次代) 和5.68% (200次代).
- 增强的算法有效地脱离了局部最佳值,特别是在以后的代阶段.
- 使用改进的FSA优化节点部署大大提高了传感器网络的整体覆盖范围.
结论:
- 增强的FSA为WSN覆盖率优化提供了一种优越的方法,其性能优于原始的FSA和基准算法.
- 提出的策略有效地提高了算法性能,融合速度,以及避免局部最佳的能力.
- 这项研究提供了一种可靠的方法来优化传感节点的部署,以实现WSN中更高的网络覆盖率.
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