文献类型：期刊文献

中文题名：基于改进瞪羚优化算法的UWSN三维定位算法

英文题名：UWSN 3D localization algorithm based on improved gazelle optimization algorithm

作者：付雷[1,2];王骥[1,2]

机构：[1]广东海洋大学电子与信息工程学院,广东湛江524088;[2]广东省智慧海洋传感网及其装备工程技术研究中心,广东湛江524088

年份：2025

卷号：40

期号：1

起止页码：80

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：北大核心2023、、EI(收录号：20250217649740)、北大核心

基金：广东省普通高校重点领域新一代信息技术专项项目(2020ZDZX3008);广东省人工智能领域重点专项项目(2019KZDZX1046)。

语种：中文

中文关键词：水下无线传感器网络;三维定位;移动节点;DV-Hop算法;瞪羚优化算法;Logistic映射;动态权重系数

外文关键词：underwater wireless sensor network;three-dimensional positioning;mobile node;DV-Hop algorithm;gazelle optimization algorithm;Logistic mapping;dynamic weight coefficient

中文摘要：为解决三维空间中的水下无线传感器网络(underwater wireless sensor networks, UWSN)传统DV-Hop算法定位误差大的问题,提出一种基于改进瞪羚优化算法(improved gazelle optimization algorithm, IGOA)的UWSN三维定位算法(IGOADV-Hop).首先,通过双通信半径修正节点跳数,对锚节点间的距离误差进行加权修正;然后,在瞪羚优化算法引入Logistic映射初始化种群,增加种群多样性;接着,在开发阶段引入位置更新动态权重系数,提升节点位置计算的全局寻优能力;最后,使用IGOA替代最小二乘法进行节点三维坐标计算,并在网络中加入移动节点和水下噪声构建动态UWSN.仿真实验结果表明,与传统DV-Hop算法和其他群智能优化算法相比,所提出算法定位精度更高.

外文摘要：To address the issue of large positioning errors in the traditional DV-Hop algorithm for underwater wireless sensor networks(UWSN)in three-dimensional spaces,an improved gazelle optimization algorithm based three-dimensional positioning algorithm for UWSN(IGOADV-Hop)is proposed.Firstly,the node hops are corrected through dual communication radii,and the distance error between anchor nodes is weighted and corrected.Secondly,the Logistic mapping is introduced into the gazelle optimization algorithm to initialize the population,increasing population diversity.Then,a dynamic weight coefficient for position updating is introduced in the exploitation stage to improve the global optimization ability of node position calculation.Finally,the IGOA is used to replace the least squares method for calculating the three-dimensional coordinates of nodes,and the dynamic UWSN is constructed by adding mobile nodes and underwater noise to the network.Simulation results show that compared with the traditional DV-Hop algorithm and other swarm intelligence optimization algorithms,the proposed algorithm has higher positioning accuracy.