基于改进樽海鞘群算法的无线传感器网络节点定位  

Localization in WSNs node based on improved salpswarm algorithm

作　　者：田洪舟 陈思溢 黄辉先 TIAN Hongzhou;CHEN Siyi;HUANG Huixian(School of Automation and Electronic Information,Xiangtan University,Xiangtan 411105,China;School of Computer Science,Guangdong University of Science and Technology,Dongguan 523000,China)

机构地区：[1]湘潭大学自动化与电子信息学院,湖南湘潭411105 [2]广东科技学院计算机学院,广东东莞523000

出　　处：《传感器与微系统》2021年第9期139-141,145,共4页Transducer and Microsystem Technologies

基　　金：湖南省教育厅一般项目(18C0091)。

摘　　要：传统距离矢量-跳数(DV-Hop)定位算法中定位阶段的准确率较低,提出了一种改进樽海鞘群算法优化的DV-Hop定位算法。首先,利用Cat混沌映射产生混沌序列,用以种群初始化;其次,在追随者更新时加入环境和优秀个体的影响,引入精英引导变异实现;最后,用败者淘汰策略去除较差个体同时产生较优个体。实验结果表明:优化后的DV-Hop算法与传统DV-Hop算法及其他对比算法相比,定位精度较高,具有更好的定位能力。Traditional DV-Hop localization algorithm has a low accuracy in the positioning stage.This paper proposes a DV-Hop localization algorithm optimized by the improved salp swarm algorithm.Firstly,Cat chaotic map is used to generate chaotic sequences for population initialization.Secondly,the influence of environment and excellent individuals is added to the follower updating,and elite guided mutation is introduced.Finally,the loser-out-tournament is used to remove the poor individuals and generate the better individuals.The experimental results show that the optimized DV-Hop algorithm has higher positioning accuracy and better positioning ability than the traditional DV-Hop algorithm and other comparative algorithms.

关 键 词：距离矢量-跳数(DV-Hop)算法 樽海鞘群算法 Cat混沌映射 精英引导变异 败者淘汰策略 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TP21[自动化与计算机技术—计算机科学与技术]