Quantitative detection of damage in layered composite materials based on particle swarm optimization and grey wolf composite algorithm
TIAN Shuxia
XU Linfeng
MA Jiangdong
CHEN Zhenmao
LOU Xuyao
LI Guangke
Abstract：A new PSO-GWO composite algorithm is proposed for the qualitative and quantitative detection of damage in layered composite materials,integrating traditional particle swarm optimization(PSO)algorithm and grey wolf optimization(GWO)algorithm.Construct a new objective function for solving damage detection problems using three identification accuracy indicators:modal flexibility matrix,frequency,and mode shape.Based on the characteristics of two traditional optimization algorithms,a nonlinear convergence factor was adopted to balance the local search ability and global search ability of the algorithm;add an adaptive local search strategy to increase the diversity of the algorithm iteration process;introducing a multi-level guided iteration strategy of the grey wolf algorithm,combined with the speed update strategy of particle swarm optimization,to compensate for the algorithm’s tendency to fall into local optima.The detection results of three different types of laminated panels show that the PSO-GWO composite algorithm has advantages in detection accuracy and convergence speed,and can achieve accurate identification of the location and degree of damage.
Keywords：Damage detectionComposite materialParticle swarm optimization algorithmGrey wolf optimization algorithmModal parameter
Publication Date：2025-12-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：9( 98-106 )