一个融合多策略灰狼优化器,用于在无线传感器网络中增强覆盖率优化
Zhenkun Liu1, Yun Ou1, Zhuo Yang2
1School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了用于高效无线传感器网络 (WSN) 覆盖的 Fusion 多策略灰狼优化器 (FMGWO). 通过使用更少的节点实现更高的覆盖率,FMGWO显著提高了WSN的部署.
科学领域:
- 计算机科学 计算机科学
- 优化算法 优化算法
- 无线传感器网络 无线传感器网络
背景情况:
- 无线传感器网络 (WSN) 对物联网,智能城市和环境监测至关重要.
- 优化WSN覆盖面对于在资源限制下有效性至关重要.
- 现有的方法面临着低效率,高计算成本和局部最佳融合等挑战.
研究的目的:
- 为WSN覆盖面开发一个先进的优化算法.
- 为了解决传统WSN优化方法的局限性.
- 提高全球覆盖效率,减少WSN部署中的计算开销.
主要方法:
- 提出了融合多策略灰狼优化器 (FMGWO),这是一个增强的灰狼优化器 (GWO).
- 集成了静电场初始化,动态参数调整,长老理事会机制,阿尔法狼旋转和混合突变策略.
- 采用除研究来验证个别策略贡献和模拟实验进行绩效评估.
主要成果:
- FMGWO在WSN覆盖率优化方面表现优于PSO,GWO,CSA,DE,GA,FA,OGWO,DGWO1和DGWO2.与PSO,GWO,CSA,DE,GA,FA,OGWO,DGWO2相比,FMGWO在WSN覆盖率优化方面表现优越.
- 只用30个节点实现高达98.63%的覆盖率,显著超过现有算法.
- 展示了改进的融合速度,增强的稳定性和更大的全球搜索能力.
结论:
- FMGWO是高效的WSN部署和覆盖率优化的高效解决方案.
- 为物联网和边缘计算中的资源受限优化问题提供了显著的优势.
- 拟议的战略共同提高人口分布,活力和全球搜索性能.
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