基于自适应调整策略灰狼算法的DV-Hop定位算法

doi:10.11896/j.issn.1002-137X.2019.05.012

摘要/Abstract

摘要： 针对无线传感器网络传统距离-矢量(DV-Hop)算法中最小二乘法估计误差过大的问题,提出了一种改进灰狼优化(Grey Wolf Optimization,GWO)算法与DV-Hop融合的算法。首先,利用传统的DV-Hop算法估算出信标节点与各未知节点间的距离。其次,用具有自适应策略的改进GWO算法代替最小二乘法来估算未知节点的位置,所做改进包括初始化狼群个体时引入佳点集,以提高初始种群的遍历性;为了加快种群位置的更新速度,对控制参数a采取自适应调整策略,并根据α,β和σ的适应度值加权更新种群位置。最后,采取镜像策略对估算出的越界节点进行处理。实验结果表明,相比于传统DV-Hop算法、文献[1]的算法和文献[2]的算法,所提算法的定位精度更高,稳定性更好。

关键词: DV-Hop算法, 灰狼优化算法, 佳点集, 无线传感器网络, 自适应策略

Abstract: Aiming at the problem of the least square estimation error in traditional distance vector-hop (DV-Hop) algorithm for wireless sensor networks,a fusion algorithm of improved grey wolf optimization(GWO) and DV-Hop was proposed.Firstly,the traditional DV-Hop algorithm is used to estimate the distance between beacon nodes and unknown nodes.Secondly,GWO algorithm with adaptive strategy is employed to replace the least square method to estimate the position of unknown nodes.The improvements include the introduction of good-points sets for initial wolves individuals to improve the ergodicity of the initial population.In order to speed up the update of population position,the control parameter a is adaptive adjusted and the population position is updated according to the fitness values of α,β and σ.Finally,the mirroring strategy is adopted to deal with the estimated cross-border node.Experimental results show that the proposed algorithm has high positioning accuracy and good stability compared with the traditional DV-Hop algorithm,the literature [1]’s algorithm and the literature [2]’s algorithm.

Key words: Adaptive adjusted strategy, DV-Hop algorithm, Good-points sets, Grey wolf optimization algorithm, Wireless sensor network

中图分类号:

TP393

孙博文, 韦素媛. 基于自适应调整策略灰狼算法的DV-Hop定位算法[J]. 计算机科学, 2019, 46(5): 77-82. https://doi.org/10.11896/j.issn.1002-137X.2019.05.012

SUN Bo-wen, WEI Su-yuan. DV-Hop Localization Algorithm Based on Grey Wolf Optimization Algorithm with
Adaptive Adjutment Strategy[J]. Computer Science, 2019, 46(5): 77-82. https://doi.org/10.11896/j.issn.1002-137X.2019.05.012

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