计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (12): 294-301.doi: 10.11896/jsjkx.250200116

• 计算机网络 • 上一篇    下一篇

基于克隆反向学习灰狼优化算法的WSNs高效分簇路由方法

陈海燕   

  1. 华东政法大学计算机科学与技术系 上海 201620
  • 收稿日期:2025-02-27 修回日期:2025-06-13 出版日期:2025-12-15 发布日期:2025-12-09
  • 通讯作者: 陈海燕(tom_chy@163.com)
  • 基金资助:
    上海市哲学社会科学项目一般课题(2021BFX003)

Efficient Clustering Routing Method for WSNs Based on Clone Reverse Learning Grey WolfOptimization Algorithm

CHEN Haiyan   

  1. Department of Computer Science and Technology, East China University of Political Science and Law, Shanghai 201620, China
  • Received:2025-02-27 Revised:2025-06-13 Published:2025-12-15 Online:2025-12-09
  • About author:CHEN Haiyan,born in 1978,master,associate professor.His main research interests include information security,artificial intelligence and big data.
  • Supported by:
    This work was supported by the General Project of Shanghai Philosophy and Social Science Program(2021BFX003).

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摘要： 针对无线传感器网络簇路由中节点能耗不均衡与簇首选择优化问题,提出一种基于克隆反向学习灰狼优化的能耗均衡路由协议(Clone Reverse Learning Grey Wolf Optimizer-based Energy-Balanced Routing Protocol,CRLGWORP)。该算法在传统灰狼优化框架中引入克隆选择机制,通过复制优质个体增强种群多样性,并结合反向学习策略扩大解空间搜索范围,有效提升全局寻优能力。设计以网络平均剩余能量和簇首到基站平均距离为优化目标的自适应加权函数,根据网络能量分布动态调整权重,平衡能量效率与通信距离的优化重点。簇首选举阶段优先选取高能量且靠近基站的节点,数据传输阶段采用多跳梯度中继机制优化通信路径,降低长距离传输能耗。实验结果表明,与LEACH,LEACH-C,HEED,FIGWO和HGWCSOA-OCHS算法相比,该算法显著延长了网络生命周期,提升了节点能量均衡性。

关键词: 无线传感器网络, 克隆, 反向学习, 灰狼优化, 能耗均衡, 动态加权

Abstract: To address the issues of uneven node energy consumption and optimal cluster head selection in clustering routing for WSNs,this paper proposes a Clone Reverse Learning Grey Wolf Optimizer-based Energy-Balanced Routing Protocol(CRLGWORP).This algorithm introduces a clone selection mechanism into the traditional grey wolf optimizer framework,enhancing population diversity by replicating high-quality individuals,and combines reverse learning strategies to expand the search space for solutions,effectively improving global optimization capabilities.An adaptive weighting function is designed with the objectives of maximizing the network’s average residual energy and minimizing the average distance from cluster heads to the base station.The weights are dynamically adjusted based on the network’s energy distribution to balance the optimization focus between energy efficiency and communication distance.In the cluster head election phase,nodes with high energy and proximity to the base station are prioritized.During the data transmission phase,a multi-hop gradient relay mechanism is employed to optimize communication paths,reducing energy consumption for long-distance transmissions.Experimental results demonstrate that,compared with LEACH,LEACH-C,HEED,FIGWO and HGWCSOA-OCHS algorithms,the proposed algorithm significantly extends the network lifespan and improves node energy balance.

Key words: Wireless sensor networks, Cloning, Reverse learning, Grey wolf optimizer, Energy balance, Dynamic weighting

中图分类号: 

  • TP393
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