火力与指挥控制2026，Vol.51Issue(1)：73-80,8.DOI:10.3969/j.issn.1002-0640.2026.01.009
基于改进POA算法优化VMD的时序信号分解方法
Optimizing Variational Mode Decomposition-based Time Series Signal Decomposition with an Improved Planetary Optimization Algorithm
白瑞 1阳周明 1范文超 2崔新悦 1张彭博1
作者信息
- 1. 北方自动控制技术研究所,太原 030006
- 2. 陆军装备部驻北京地区军事代表局驻太原地区第二军事代表室,太原 030006
- 折叠
摘要
Abstract
To address the difficulty in parameter selection in Variational Mode Decomposition(VMD),an Improved Planetary Optimization Algorithm(POA),Enhanced Planetary Optimization Algorithm(EPOA),is proposed.By utilizing cubic chaotic initialization,an elite opposition-based learning strategy,and nonlinear factors,the POA is improved to enhance its performance in solving specific optimization problems.With the minimum envelope entropy as the fitness function,this algorithm optimizes the mode number K and penalty factor α in VMD,and its performance is compared with that of the POA,Grey Wolf Optimizer(GWO),and Particle Swarm Optimization(PSO)algorithm.The results indicate that the improved algorithm can converge to a better solution faster than the comparative algorithms.This provides an effective solution for parameter selection in VMD.关键词
变分模态分解/POA/Cubic混沌初始化/反向学习/非线性因子Key words
VMD/POA/cubic chaotic initialization/elite opposition-based learning/nonlinear factor分类
信息技术与安全科学引用本文复制引用
白瑞,阳周明,范文超,崔新悦,张彭博..基于改进POA算法优化VMD的时序信号分解方法[J].火力与指挥控制,2026,51(1):73-80,8.
