基于创新的混合算法在列车姿态测量系统中的应用研究  

Research on Application of Innovative Hybrid Algorithm in Train Attitude Measurement System

作　　者：雷丽婷 蒋常升 师光洲 Lei Li-ting;Jiang Chang-sheng;Shi Guang-zhou(Liuzhou Railway Vocational Technical College,Guangxi Liuzhou 545616)

机构地区：[1]柳州铁道职业技术学院,广西柳州545616

出　　处：《内燃机与配件》2024年第14期22-24,共3页Internal Combustion Engine & Parts

基　　金：2022年度柳州铁道职业技术学院科技类立项项目“基于无线传感器网络的列车运行姿态测量系统研究”(课题编号:2022-KJA04)。

摘　　要：本文针对列车姿态测量系统中无线传感器网络的覆盖优化难题,提出了一种创新的混合算法—PGSO。该算法融合了粒子群算法优化出色的全局搜索能力与萤火虫算法在局部精细搜索方面的优势,旨在提升列车姿态测量系统中无线传感器网络的覆盖质量。实验显示,PGSO算法较PSO和GSO更快收敛、覆盖率更高、全局搜索能力更强,在列车姿态测量场景中表现卓越,有效解决了无线传感器网络覆盖优化问题。This paper proposes an innovative hybrid algorithm,PGSO,to address the challenging issue of wireless sensor network coverage optimization in the train attitude measurement system.This algorithm combines the excellent global search capability of Particle Swarm Optimization(PSO)with the local fine-grained search advantage of Glowworm Swarm Optimization(GSO),aiming to improve the coverage quality of the wireless sensor network in the train attitude measurement system.Experiments show that the PGSO algorithm converges faster,has higher coverage,and stronger global search capabilities compared to PSO and GSO.It excels in the train attitude measurement scenario,effectively solving the wireless sensor network coverage optimization problem.

关 键 词：列车姿态测量系统 无线传感器网络 覆盖优化 粒子群优化(PSO) 萤火虫算法(GSO) 混合算法(PGSO) 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]