计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 317-325.doi: 10.11896/jsjkx.230300019

• 计算机网络 • 上一篇    下一篇

基于改进型白鲸算法的RFID网络规划

陈奕君1,2, 郑嘉利1,2, 李芷芊1,2, 张江波1,2, 朱兴洪1   

  1. 1 广西大学计算机与电子信息学院 南宁530004
    2 广西多媒体通信与网络技术重点实验室 南宁530004
  • 收稿日期:2023-03-03 修回日期:2023-05-24 出版日期:2024-03-15 发布日期:2024-03-13
  • 通讯作者: 郑嘉利(zjl@gxu.edu.cn)
  • 作者简介:(moleelz@163.com)
  • 基金资助:
    :国家自然科学基金(62366004)

Improved Beluga Whale Optimization for RFID Network Planning

CHEN Yijun1,2, ZHENG Jiali1,2, LI Zhiqian1,2, ZHANG Jiangbo1,2 , ZHU Xinghong1   

  1. 1 School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China
    2 Guangxi Key Laboratory of Multimedia Communications and Network Technology,Nanning 530004,China
  • Received:2023-03-03 Revised:2023-05-24 Online:2024-03-15 Published:2024-03-13
  • About author:CHEN Yijun,born in 1997,postgra-duate.Her main interest is RFID network planning.ZHENG Jiali,born in 1979,professor.His main research interests include Internet of Things,RFID and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(62366004).

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摘要： 随着射频识别(Radio Frequency Identification,RFID)技术的发展,人们对其应用的要求越来越高,在阅读器部署方面的研究也逐渐深入。为了解决规定区域内RFID阅读器位置规划问题,在划定的区域内,以标签覆盖率、阅读器间的碰撞干扰、负载均衡为目标来建立数学优化模型,在白鲸算法的基础上提出了一种改进型白鲸算法。首先,针对标准白鲸算法存在易陷入局部最优、丢失次优解的缺陷,提出了一种更新精英群体机制;其次,为了增强算法的探索能力,加入了反向学习策略;最后,运用该算法来解决RFID网络规划问题。通过在一定环境中放置不同数量集群和随机分布的标签,将改进型白鲸算法与粒子群算法、灰狼算法和标准白鲸算法进行对比。仿真结果表明,在相同环境下,改进型白鲸算法的性能相比粒子群算法平均提高了21.1%,比灰狼算法提高了28.5%,比白鲸算法提高了3.3%,说明该算法相比其他3种算法在搜索精度上具有更好的性能,并通过阅读器优化部署测试,验证了该应用的有效性和可行性。

关键词: 射频识别, 阅读器部署, 白鲸算法, 反向学习, 网络规划

Abstract: With the development of radio frequency identification(RFID) technology,the demand for its application is getting higher and higher,and the research in reader deployment is gradually deepening.In order to solve the RFID reader location planning problem in the defined area,a mathematical optimization model is established with the objectives of tag coverage,collision interference between readers and load balancing in the delimited area,and an improved beluga whale optimization is proposed on the basis of the beluga whale optimization.Firstly,to address the shortcomings of the standard beluga whale optimization,which is easy to fall into the local optimum and lose the suboptimal solution,an update elite group mechanism is proposed.Secondly,to enhance the exploration capability of the algorithm,an opposition-based learning strategy is added,Finally,the algorithm is applied to solve the RFID network planning problem.By placing different numbers of clusters and randomly distributed tags in a certain environment,the improved beluga whale optimization is compared with the particle swarm algorithm,the gray wolf algorithm and the standard beluga whale optimization and the results are derived.Simulation results show that the performance of the improved beluga whale optimization improves on average 21.1% over the particle swarm optimization,28.5% over the grey wolf optimizer,and 3.3% over the beluga whale optimization in the same environment,indicating that the algorithm has better performance than the other three algorithms in terms of search accuracy,then,the effectiveness and feasibility of the application are verified by reader optimization deployment tests.

Key words: RFID, Reader deployment, Beluga whale optimization, Opposition-based learning, Network planning

中图分类号: 

  • TP301.6
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