多策略改进麻雀搜索算法优化三维DV-Hop节点定位

doi:10.13229/j.cnki.jdxbgxb.20220530

摘要/Abstract

摘要：

为进一步提升传统3DDV-Hop算法的定位精度和稳定性，提出了一种利用多策略改进麻雀搜索算法（MISSA）优化3DDV-Hop的定位算法（MISSA-3DDV-Hop）。首先，采用通信半径分级方法细化多通信半径定位节点的跳数值，提高了最小跳数计算的准确性。然后，利用跳距误差与估计距离误差的加权平均值修正节点间的平均跳距，降低了信标节点与未知节点之间的距离估算误差。最后，采用麻雀搜索算法实现3DDV-Hop算法未知节点的位置寻优，并引入佳点集和发现者-跟随者自适应调整策略，通过建立误差适应度函数和目标函数，增强麻雀搜索算法初始种群的分布性和多样性以及全局收敛速度和局部寻优能力。仿真结果表明，与传统3DDV-Hop、IPSO-3DDV-Hop和IGA-3DDV-Hop算法相比，本文方法具有更高的定位精度，更好的稳定性和更快的收敛速度。

关键词: 通信技术, 无线传感网络, 3DDV-Hop, 多策略麻雀搜索算法, 平均跳距, 佳点集

Abstract:

To enhance the node localization accuracy and stability of the tradition three-dimensional DV-Hop （3DDV-Hop） algorithm in wireless sensor network （WSN）， a 3DDV-Hop positioning optimization algorithm based on the improved multi strategy sparrow search algorithm （MISSA-3DDV-Hop） was proposed. Firstly， a communication radius classification method was used in the anchor nodes to refine the hop value of positioning nodes， which improves the accuracy of the calculation of the minimum hop number. Then the weighted average of hop distance error and estimated distance error was applied to correct the average hop distance between nodes， so as to reduce the distance estimation error between anchor nodes and unknown nodes. Finally， the sparrow search algorithm was used to optimize the location of unknown nodes in 3DDV-Hop algorithm， and the good-point set and discoverer-follower adaptive adjustment strategy were introduced. By establishing the error fitness function and objective function， the distribution and diversity of the initial population， global convergence speed and local optimization ability of sparrow search algorithm were enhanced. The simulation results show that the improved algorithm effectively improves the positioning accuracy and convergence speed compared with the traditional 3DDV-Hop， IPSO-3DDV-Hop and IGA-3DDV-Hop algorithms.

Key words: communication technology, wireless sensor network, 3DDV-Hop, multi-strategy sparrow search algorithm, average hop distance, good point set

中图分类号:

TP393

段中兴,刘瑞兴,刘冲. 多策略改进麻雀搜索算法优化三维DV-Hop节点定位[J]. 吉林大学学报(工学版), 2024, 54(3): 771-784.

Zhong-xing DUAN,Rui-xing LIU,Chong LIU. 3DDV⁃Hop node localization optimized based on multi⁃strategy improved sparrow search algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(3): 771-784.

图/表 14

图1

图2

图3

图4

表1

麻雀搜索算法参数初始化"

参数	数值
种群数量 $N$	100
迭代次数 $t m a x$	200
安全阈值 $S T$	0.8
比例系数 $γ$	0.1
扰动偏离因子 $α$	0.1

表1

表2

网络参数初始化"

参数	数值
网络区域大小 $V$ /m³	$100 × 100 × 100$
通信半径 $R$ /m	30
节点总数 $N A$ /个	200
信标节点 $B A$ /个	60
未知节点 $U N A$ /个	140
初始跳数 $h$	0
循环次数 $l o o p$	500
节点能量	充足
节点分布	随机分布

表2

图5

图6

图7

图8

图9

图10

图11

图12

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