噪声环境下RSSI定位问题及其求解的果蝇优化算法  

RSSI localization in noisy environment and its fruit fly optimization algorithm

作　　者：郝娟 张著洪 凃歆 HAO Juan;ZHANG Zhuhong;TU Xin(College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵阳550025

出　　处：《计算机工程与应用》2018年第7期121-126,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61563009);贵州大学创新基金(研理工2017013)

摘　　要：针对未知节点的定位过度依赖于接收信号强度指示(Received Signal Strength Indicator,RSSI)物理测量的精度问题,将传统RSSI定位模型转化为非约束期望值规划模型,进而设计随机环境下的新型果蝇优化算法寻找未知节点的位置。该算法利用弧形分组将果蝇群均衡划分为子群,对果蝇个体实施混合变异,加速寻优进程,提高收敛速度和寻优精度。比较性的数值实验显示,该算法的收敛速度快,对未知节点的定位精度高,其应用于RSSI定位问题是可行的。This paper firstly transforms the conventional RSSI positioning model into a non-constrained expected value programming model.Secondly,one such model is solved by developing a novel fruit fly optimization algorithm in stochastic environments in order to seek the location of the unknown node.In this algorithm,the current fruit fly population is divided into sub-populations by arc grouping,and subsequently a hybrid mutation strategy is implemented to find the optimal solution.Comparative numerical experiments have validated that the algorithm,with high-efficient convergence and high positioning accuracy,is feasible in solving engineering RSSI positioning.

关 键 词：接收信号强度指示 期望值规划 果蝇优化 弧形分组 混合变异 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]