WSN中基于灰狼和差分进化的簇首选择算法  

Cluster head selection algorithm based on gray wolf and differential evolution in WSN

作　　者：张新 俞宗佐 ZHANG Xin;YU Zong-zuo(College of Computer Science and Technology,Inner Mongolia Normal University,Huhhot 010000,China)

机构地区：[1]内蒙古师范大学计算机科学技术学院,内蒙古呼和浩特010000

出　　处：《计算机工程与设计》2023年第2期356-363,共8页Computer Engineering and Design

基　　金：内蒙古自然科学基金项目(2020MS06029、2020LH06009);内蒙古自治区科技计划基金项目(2020GG0165)。

摘　　要：为更好均衡无线传感器网络(WSN)的网络能耗、延长网络的生命周期,提出一种基于灰狼优化和差分进化的簇首选择算法。将差分进化算法混合到灰狼优化算法中,调整混合算法中的收敛因子与缩放因子,避免灰狼优化算法易陷入停滞状态的缺点;使用节点剩余能量及节点与基站的距离设计适应度函数,保证簇首的全局最优,使网络中节点的能量消耗更均衡。仿真结果表明,与LEACH协议、LEACH-E协议以及FIGWO协议相比,所提簇首选择算法能够更有效地均衡网络的能耗,延长网络的生存时间。To better balance the network energy consumption of wireless sensor networks(WSN) and prolong the network life cycle, a cluster head selection algorithm based on gray wolf optimization and differential evolution was proposed. The differential evolution algorithm was mixed into the gray wolf optimization algorithm, and the convergence factor and scaling factor in the hybrid algorithm were adjusted to avoid the disadvantage that the gray wolf optimization algorithm is easy to fall into a stagnant state. The residual energy of the node and the distance between the node and the base station were used to design the fitness function, to ensure the global optimality of the cluster head, so that the energy consumption of the nodes in the network was more balanced. Simulation results show that, compared with LEACH protocol, LEACH-E protocol and FIGWO protocol, the proposed cluster head selection algorithm can more effectively balance the energy consumption of the network and prolong the network lifetime.

关 键 词：无线传感器网络 簇首选择 能量均衡 网络生命周期 差分进化算法 灰狼优化算法 混合算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]