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增强的秘书鸟优化算法用于在无线传感器网络中节能选择集群头

Ketty Siti Salamah¹, Dadang Gunawan¹, Ajib Setyo Arifin¹

¹Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia.

Sensors (Basel, Switzerland)

|March 14, 2026

概括

本研究介绍了一种增强的秘书鸟优化算法 (ESBOA),用于在无线传感器网络 (WSN) 中高效地选择集群头. 通过优化集群形成,ESBOA提高了网络寿命和能源平衡.

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:

集群头 (CH) 选择对于无线传感器网络 (WSN) 的能量平衡和网络寿命至关重要.
现有的CH选择的元启发方法面临着诸如搜索多样性有限和过早融合等挑战,导致能量消耗不均.

研究的目的:

在WSNs中解决CH选择的NP-hard优化问题.
为能源意识的CH选择提出一个增强的秘书鸟优化算法 (ESBOA).
改善WSNs中的网络寿命和能源效率.

主要方法:

制定了CH选择作为一个多标准的能源意识优化问题.
通过整合基于地图的物流混乱人口初始化和代本地搜索机制来开发ESBOA.
使用了多个标准的健身功能,考虑了剩余能量,距离基站的距离和节点程度.
在Python 3.11.9中使用一级无线电能量模型实现了框架.

主要成果:

与标准的SBOA,CPO和DBO相比,ESBOA表现优越.
拟议的方法保留了更多的活节点,并保持了更高的剩余能量.
与标准的SBOA相比,ESBOA在最后一个节点死亡 (LND) 中取得了大约3-13%的改善.

结论:

ESBOA有效地提高了能源效率,并延长了WSN中的网络寿命.
混沌初始化和局部搜索的整合显著改善了CH选择.
ESBOA为优化WSN性能提供了一个有前途的解决方案.

关键词:

集群头的选择集群头的选择节能集群就是节能集群.进行元启发式优化优化.秘書鳥優化算法秘書鳥優化算法无线传感器网络是无线传感器网络.

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