Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (3): 317-325.doi: 10.11896/jsjkx.230300019

• Computer Network • Previous Articles     Next Articles

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).

PDF (PC)

87

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

CLC Number: 

  • TP301.6
[1]MA L,HU K,ZHU Y,et al.Cooperative Artificial Bee ColonyAlgorithm for Multi-objective RFID Network Planning[J].Journal of Network and Computer Applications,2014,42(6):143-162.
[2]CHENG P,SU J P.Research and Application of RFID Square Vehicle Positioning system[J].Knitting Industry,2018(12):6-10.
[3]YANG C Y,WANG X Y,MAO S W.RFID Tag LocalizationWith a Sparse Tag Array[J].IEEE Internet of Things Journal,2022,9(18):16976-16989.
[4]FAN Q,WU S,ZHOU X,et al.A Genetic Algorithm Based on Auxiliary-Individual-Directed Crossover for Internet-of-Things Applications[J].IEEE Internet of Things Journal,2021,8(7):5518-5530.
[5]GUAN Q,LIU Y,YANG Y,et al.Genetic Approach for Network Planning in the RFID Systems[C]//International Confe-rence on Intelligent Systems Design & Applications.IEEE Computer Society,2006:897-903.
[6]LIU H L,CHEN L,DEB K,et al.Investigating the Effect of Im-balance Between Convergence and Diversity in Evolutionary Multi-objective Algorithms[J].IEEE Transactions on Evolutionary Computation,2017,21(3):408-425.
[7]KENNEDY J,EBERHART R.Particle Swarm Optimization[C]//Proceedings of IEEE IntErnational Conference Neural Network,4th Perth.WA,Australia,2002:1942-1948.
[8]LI J,TAN Y.A Comprehensive Review of the Fireworks Algorithm [J].ACM Computing Surveys (CSUR),2019,52(6):1-28.
[9]MIRJALILI M,MIRJALILI S M,LEWIS A.Grey Wolf Optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[10]CORUS D,OLIVETO P S.Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms[J].IEEE Transactions on Evolutionary Computation,2018,22(5):720-732.
[11]ZHANG R,GUO Y,HUANG S H,et al.3D RFID NetworkOptimization Based on Improved Fire-fly Algorithm[J].Computer Engineering and Design,2019,40(10):2731-2735,2772.
[12]CAO Y,LIU J,XU Z.A Hybrid Particle Swarm Optimization Algorithm for RFID Network Planning[J].Soft Computing,2021(25),5747-5761.
[13]QUAN Y X,ZHENG J L,LUO W C,et al.RF-ID NetworkPlanning Based on Improved Gray Wolf Algorithm[J].Compu-ter Science,2021,48(1):253-257.
[14]SHI W G,WANG W,YU Y,et al.Optimal Deployment ofPhased Array Antennas for RFID Network Planning Based on an Improved Chicken Swarm Optimization[J].IEEE Internet of Things Journal,2021,8(19):14572-14588.
[15]XIE X D,ZHENG J L,FENG M Y,et al.Multi-Objective Mayfly Optimization Algorithm Based on Dimensional Swap Variation for RFID Network Planning[J].IEEE Sensors Journal,2022,22(7):7311-7323.
[16]ZHANG J B,ZHENG J L,XIE X D,et al.Mayfly SparrowSearch Hybrid Algorithm for RFID Network Planning[J].IEEE Sensors Journal,2022,22(16):16673-16686.
[17]LIU J H,LIU J.A Decomposition-based Multi-objective Self-adaptive Differential Evolution Algorithm for RFID Network Planning[C]//2020 IEEE Congress on Evolutionary Computation (CEC).IEEE,2020:1-7.
[18]ZHONG C T,LI G,MENG Z.Beluga Whale Optimization:A Novel Nature-Inspired Metaheuris-tic Algorithm[J].Know-ledge-Based Systems,2022,251(109215):1-23.
[19]XIE X D,ZHENG J L,LIN Z H,et al.Random Mating Mayfly Algorithm for RFID Network Planning[J].The Journal of China Universities of Posts and Telecommunications,2022,29(5):40-50.
[20]WANG Q X,GUO X B.Particle Swam Optim-ization Algorithm Based on Levy Flight[J].Computer Application Research,2016,33(9):2588-2591.
[21]LIU L,FU S C,HUANG H X.Grey Wolf Opti-mization Algorithm Based on Drunkard's Walk and Reverse Learning[J].Computer Engineering and Science,2021,43(9):1558-1566.
[22]DEB,K,PRATAP A,AGARWAL S,et al.A Fast and Elitist Multi-Objective Genetic Algorithm:NSGA-II[J].IEEE Tran-sactions on Evolutionary Computation,2002,6(2):182-197.
[23]TIZHOOSH H R.Opposition-Based Learning:A New Scheme for Machine Intelligence[C]//International Conference on Computational Intelligence for Modeling,Control and Automation and International Conference on Intelligent Agents,Web Technologies and Internet Commerce.2005:695-701.
[24]WANG H,WU Z,RAHNAMAYAN S,et al. Enhancing Particle Swarm Optimization Using Generalized Opposition-Based Learning[J].Information Sciences,2011,181(20):4699-4714.
[25]SHARMA H,BANSAL J C,ARYA K V.Opposition BasedLévy Flight Artificial Bee Colony[J].Memetic Computing,2013,5(3):213-227.
[26]CHEN J H,HU Y,RAO J L,et al.Optimal Power Flow Calcu-lation Based on Adaptive Opposition-based Learning Bald Eagle Search Algorithm[J].Electrotechnical Materials,2023(1):85-93.
[27]DUAN W,XU B.Constrained Differential Evolution Algorithm Based on Opposition-Based Learning and Feasibility Rule Crossover[J].Computer Applications and Software,2022,39(10):259-265,335.
[1] ZHAO Yang, LI Lingyun, ZHAO Xiaoxia, LIU Xianhui, ZHANG Liang. Missing Localization Characteristic Estimation Algorithm for Passive UHF RFID Tag [J]. Computer Science, 2023, 50(6A): 220500055-6.
[2] LUO Wen-cong, ZHENG Jia-li, QUAN Yi-xuan, XIE Xiao-de, LIN Zi-han. Optimized Deployment of RFID Reader Antenna Based on Improved Multi-objective Salp Swarm Algorithm [J]. Computer Science, 2021, 48(9): 292-297.
[3] DUAN Wen, ZHOU Liang. Redundant RFID Data Removing Algorithm Based on Dynamic-additional Bloom Filter [J]. Computer Science, 2021, 48(8): 41-46.
[4] LI Li, ZHENG Jia-li, LUO Wen-cong, QUAN Yi-xuan. RFID Indoor Positioning Algorithm Based on Proximal Policy Optimization [J]. Computer Science, 2021, 48(4): 274-281.
[5] YAN Jia-jie, ZU Jia-chen, HU Gu-yu, SHAO Dong-sheng, WANG Shuai-hui. Earth Station Network Planning Method for Heterogeneous Communication Satellites [J]. Computer Science, 2021, 48(3): 275-280.
[6] QUAN Yi-xuan, ZHENG Jia-li, LUO Wen-cong, LIN Zi-han, XIE Xiao-de. Improved Grey Wolf Optimizer for RFID Network Planning [J]. Computer Science, 2021, 48(1): 253-257.
[7] XU He, WU Man-xing, LI Peng. RFID Indoor Relative Position Positioning Algorithm Based on ARIMA Model [J]. Computer Science, 2020, 47(9): 252-257.
[8] LI Li,ZHENG Jia-li,WANG Zhe,YUAN Yuan,SHI Jing. RFID Indoor Positioning Algorithm Based on Asynchronous Advantage Actor-Critic [J]. Computer Science, 2020, 47(2): 233-238.
[9] FENG An-qi, QIAN Li-ping, HUANG Yu-pin, WU Yuan. RFID Data-driven Vehicle Speed Prediction Using Adaptive Kalman Filter [J]. Computer Science, 2019, 46(4): 100-105.
[10] HOU Pei-guo, WANG Zhi-xuan, YAN Chen. Improvement of Anti-collision Algorithm Based on RFID Tag [J]. Computer Science, 2019, 46(11A): 359-362.
[11] LI Lu-lu, DONG Qing-kuan, CHEN Meng-meng. Cloud-based Lightweight RFID Group Tag Authentication Protocol [J]. Computer Science, 2019, 46(1): 182-189.
[12] YANG Zi-wei, ZHENG Jia-li, YUE Shi-bin, YUAN Yuan, SHI Jing. New Q Value Anti-collision Algorithm Based on Label Grouping [J]. Computer Science, 2018, 45(9): 152-155.
[13] YU Wei-wei,XIE Cheng-wang. Hybrid Particle Swarm Optimization with Multiply Strategies [J]. Computer Science, 2018, 45(6A): 120-123.
[14] ZOU Hua-fu,XIE Cheng-wang,ZHOU Yang-ping,WANG Li-ping. Group Search Optimization with Opposition-based Learning and Differential Evolution [J]. Computer Science, 2018, 45(6A): 124-129.
[15] JIA Wei, HUA Qing-yi, ZHANG Min-jun, CHEN Rui, JI Xiang and WANG Bo. Mobile Interface Pattern Clustering Algorithm Based on Improved Particle Swarm Optimization [J]. Computer Science, 2018, 45(4): 220-226.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.