Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations  

作　　者：Jiaying Shen Donglin Zhu Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li 

机构地区：[1]School of Computer Science and Technology,Zhejiang Normal University,Jinhua,321004,China [2]School of Future Technologies,Jiangxi Institute of Applied Science and Technology,Nanchang,330000,China [3]School of Computing and Artificial Intelligence,Southwestern University of Finance and Economics,Chengdu,611130,China

出　　处：《Computers, Materials & Continua》2025年第1期345-369,共25页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China(Nos.62272418,62102058);Basic Public Welfare Research Program of Zhejiang Province(No.LGG18E050011);the Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education under Grant ADIC2023ZD001,National Undergraduate Training Program on Innovation and Entrepreneurship(No.202410345054).

摘　　要：The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关 键 词：Particle swarm optimization effective coverage area global optimization base station deployment 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]