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
雷丽婷 1蒋常升 1师光洲1
作者信息
- 1. 柳州铁道职业技术学院,广西 柳州 545616
- 折叠
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—PG-SO.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.
Abstract
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,effec-tively 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)Key words
Train attitude measurement system/Wireless sensor network/Coverage optimization/Particle swarm optimization(PSO)/Glowworm swarm optimization(GSO)/Hybrid algorithm(PGSO)引用本文复制引用
出版年
2024
NETL
NSTL
万方数据