改进灰狼算法的无线传感器网络覆盖优化  

Coverage Optimization of WSN Based on Improved Grey Wolf Optimizer

作　　者：范星泽 禹梅[1] FAN Xing-ze;YU Mei(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学控制与计算机工程学院,北京102206

出　　处：《计算机科学》2022年第S01期628-631,共4页Computer Science

摘　　要：如何利用移动节点实现覆盖的最大化并减少能量的使用是研究无线传感器网络的一个重要方向。基于Circle映射,改进了莱维飞行策略;结合能量位置融合机制,用优化后的灰狼算法对无线传感器网络覆盖问题进行求解。首先,引入的Circle映射大幅改善了狼群的多样性,从而能实现更加有力的搜索;其次,改进后的莱维飞行策略平衡了不同时期对全局搜索和局部寻优的需求,一定程度上加快了搜索进程,提高了收敛速度;最后考虑能量和位置的交融,每个个体不再单一考虑位置,而是结合一部分能量因素来进行移动。仿真结果表明,未考虑能量受限的改进后的灰狼算法较基本灰狼算法覆盖率有所提升,和其他文献中的算法相比,也具有更高的收敛速度和覆盖率。在考虑能量受限以后,不但保证了覆盖率,还延长了节点寿命。How to use mobile nodes to maximize coverage and reduce energy consumption is an important direction in the research of wireless sensor networks.A grey wolf optimization(GWO)algorithm is proposed to solve the coverage problem of wireless sensor network by using the improved Levy flight strategy and the energy position fusion mechanism based on the circle mapping.Simulation results show that the improved GWO without considering energy has higher convergence speed and bigger coverage rate than the basic GWO and other related algorithms.After considering the energy,the coverage can still be guaranteed and the node life can be extended.

关 键 词：无线传感器网络 覆盖优化 灰狼算法 莱维飞行 能量位置融合 

分 类 号：TP393.0[自动化与计算机技术—计算机应用技术]