基于混沌改进灰狼优化的WSNs分簇路由算法  

Clustering routing algorithm for WSNs based on chaotic improved GWO

作　　者：侯华[1] 蔡剑平 周佳明 HOU Hua;CAI Jianping;ZHOU Jiaming(School of Information and Electrical Engineering,Hebei University of Engineering,Handan 056000,China)

机构地区：[1]河北工程大学信息与电气工程学院,河北邯郸056000

出　　处：《传感器与微系统》2024年第12期140-144,共5页Transducer and Microsystem Technologies

基　　金：河北省自然科学基金资助项目(F2021402009)。

摘　　要：针对无线传感器网络(WSNs)节点能耗负载不均衡的问题,提出一种基于混沌改进灰狼优化(CIGWO)的分簇路由算法。通过混沌化原GWO算法的参数,并在迭代中添加混沌映射种群,改进灰狼种群位置更新策略,提高GWO算法全局搜索能力;根据节点的平均剩余能量设计适应度函数,选择全局最优的簇首,降低网络的能量消耗。仿真结果表明:所提出的算法与LEACH,HEED和基于适应度改进的GWO(FIGWO)算法相比,能够有效地均衡节点负载,降低网络能耗,延长网络寿命。Aiming at the problem of unbalanced energy consumption and load of wireless sensor networks(WSNs),a clustering routing algorithm based on chaotic improved grey wolf optimization(CIGWO)is proposed.By chaotization of parameters of original GWO algorithm and adding chaotic mapping population in iteration,the strategy of grey population location update is improved and the global search ability of GWO algorithm is enhanced.According to the average residual energy of nodes fitness function is designed selected globally the optimal cluster head reduce the energy consumption of the network.Simulation results show that compared with LEACH,HEED and fittness improved GWO(FIGWO)algorithms,the proposed algorithm can effectively balance node load,reduce network energy consumption,and extend network life.

关 键 词：无线传感器网络 灰狼优化 混沌映射 能耗均衡 簇首选择 路由算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]