摘要
针对无线传感器网络定位节点容易受到测距误差影响的问题,提出一种自适应调整惯性权重的分群进化式定位算法.以当前粒子与种群最优值之间的差异度为依据,改进算法对惯性权重做非线性调整,抑制局部最优和早熟收敛.通过粒子筛选进化进程将种群划分成以优胜粒子为中心的多个小群,搜索各个小群的区域解空间,在个体最优和小群最优的引导下对种群最优解集定期更新,缩小搜索区域,提高粒子收敛速度.仿真结果表明改进算法对测距误差具有较好的鲁棒性,节点定位精度有了明显的提高.
Aiming at the problems that locating nodes of wireless sensor network are easy to be interfered,a localization algorithm is proposed based on grouping particles swarm evolution and inertia weight adaptive adjustment.To suppress disadvantage of locally optimum and premature convergence,the proposed algorithm makes nonlinear adjustment for the inertia weight with the diversity of current particle and swarm optimization.The population is divided into several small swarms centralized with superior particle to search in each regions of solution space and to update best solutions in population regularly led by personal best and small swarm best,which reduces searching area and improve the convergence speed. Simulation shows that the proposed algorithm has good robustness to range error,and obviously improve localization accuracy.
出处
《微电子学与计算机》
CSCD
北大核心
2015年第9期128-132,共5页
Microelectronics & Computer
基金
国家自然科学基金(61202007)
广西教育厅科研项目(KY2015ZD143)
桂林航天工业学院基金项目(YJ1302)
关键词
无线传感器网络
节点定位
分群粒子群
自适应
wireless sensor networks
node localization
grouping particle swarm
adaptive
