[1]任庆欣,冯锋*.基于Sobol-Halton序列ZOA-GWO的WSN覆盖研究[J].计算机技术与发展,2025,(05):1-8.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0388]
　REN Qing-xin,FENG Feng*.Research on WSN Coverage Based on Sobol-Halton Sequence ZOA-GWO[J].,2025,(05):1-8.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0388]
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基于Sobol-Halton序列ZOA-GWO的WSN覆盖研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年05期
页码:
1-8
栏目:
分布与并行计算
出版日期:
2025-05-10

文章信息/Info

Title:
Research on WSN Coverage Based on Sobol-Halton Sequence ZOA-GWO
文章编号:
1673-629X(2025)05-0001-08
作者:
任庆欣; 冯锋*
宁夏大学 信息工程学院,宁夏 银川 750021
Author(s):
REN Qing-xin; FENG Feng*
School of Information Engineering,Ningxia University,Yinchuan 750021,China
关键词:
无线传感器网络; WSN覆盖优化; 斑马优化算法; 灰狼优化算法; Sobol序列; Halton序列
Keywords:
wireless sensor network; WSN coverage optimization; zebra optimization algorithm; grey wolf optimization algorithm; Sobol sequence; Halton sequence
分类号:
TP393.0
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0388
摘要:
针对无线传感器(Wireless Sensor Network)随机部署时产生的节点分布不均,从而导致覆盖率低的问题,提出了一种基于 Sobol-Halton 序列的斑马优化算法与灰狼优化算法(ZOA-GWO)相结合的 WSN 覆盖优化方法。 首先,利用 Sobol-Halton 序列随机产生分布节点,其旨在初始化 WSN 节点时具有更优的随机性,使得随机生成的节点更加均匀,间接提高部署 WSN 网络时的覆盖率和连通性。 其次,将斑马优化算法(ZOA)与灰狼优化算法(GWO)相融合,相比 GWO 算法,ZOA算法在前期有着更快的迭代速度,局部搜索率更高,而 GWO 算法在后期迭代速率更快,能够平衡全局搜索能力和局部搜索能力的精度。 将融合后的算法分别应用于迭代过程的前期与后期能够确保 WSN 部署优化的整体性能。 最后,用四个基准测试函数分别对 GWO、ZOA、ZOA-GWO、S-ZOA-GWO(加入 Sobol 序列初始化种群的融合算法)、SH-ZOA-GWO(加入 Sobol 和 Halton 序列初始化种群的融合算法)进行仿真,并将 ZOA 的 WSN 覆盖优化、GWO 的 WSN 覆盖优化、基于 Sobol-Halton 序列 ZOA-GWO 的 WSN 覆盖优化效果进行对比实验,证明了该方法的有效性和先进性。
Abstract:
Aiming at the problem of low coverage due to uneven distribution of nodes during random deployment of Wireless Sensor net-works,an optimization method of WSN coverage based on Sobol - Halton sequence is proposed,which combines zebra optimization algorithm and grey wolf optimization algorithm (ZOA-GWO). Firstly,Sobol-Halton sequence is used to randomly generate distributed nodes,which aims to have better randomness when initializing WSN nodes,make randomly generated nodes more uniform,and indirectly improve coverage and connectivity when deploying WSN networks. Secondly,Zebra Optimization Algorithm ( ZOA) and Grey Wolf Optimization algorithm (GWO) are combined. Compared with GWO,ZOA has faster iteration speed and higher local search rate in the early stage,while GWO has faster iteration rate in the later stage,which can balance the accuracy of global search ability and local search ability. Applying the fused algorithm to the early and late stages of the iterative process can ensure the overall performance of WSN de-ployment optimization. Finally,four benchmark functions are used to simulate GWO, ZOA, ZOA - GWO, S - ZOA - GWO ( fusion algorithm with Sobol sequence to initialize the population) and SH-ZOA-GWO ( fusion algorithm with Sobol and Halton sequence to initialize the population), and the WSN coverage optimization of ZOA, WSN coverage optimization of GWO, and WSN coverage optimization based on Sobol-Halton are compared and tested to prove the effectiveness and advancement of the proposed method.

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更新日期/Last Update: 2025-05-10