An Improved PSO Algorithm and Its Application to Structural Damage Detection

Author(s):  
Ling Yu ◽  
Zu-yong Wan
2014 ◽  
Vol 919-921 ◽  
pp. 338-343 ◽  
Author(s):  
Ling Yu ◽  
Yong Ming Fu

In order to solve the inverse problem on structural damage detection (SDD) in the field of structural health monitoring (SHM), a FGAPSO algorithm is proposed by a fusion of the genetic algorithm (GA) and the particle swarm optimization (PSO) in this study. For improving the simple GA with drawbacks of easy precocious and of lower computation efficiency, the real-coded GA is implemented, the chaotic logistic mapping is chosen for initializing population, the self-adaptive crossover-mutation operators and elitist strategy are employed. The GA is then mixed with the PSO algorithm for the population diversity and convergence by exchanging genes between two new populations internally and the goal of improving GA is attained at last. Further, some numerical simulations on a 13-bar planar truss structure with several damage cases have been carried out for assessing the performance of the FGAPAO. The illustrated results show that the proposed FGAPSO algorithm is better than any of conventional GA and PSO. Even for the slight damage case, it is still more feasible and effective for SDD.


2021 ◽  
Vol 11 (11) ◽  
pp. 5144
Author(s):  
Xiao-Lin Li ◽  
Roger Serra ◽  
Julien Olivier

In the past few decades, vibration-based structural damage detection (SDD) has attracted widespread attention. Using the response data of engineering structures, the researchers have developed many methods for damage localization and quantification. Adopting meta-heuristic algorithms, in which particle swarm optimization (PSO) is the most widely used, is a popular approach. Various PSO variants have also been proposed for improving its performance in SDD, and they are generally based on the Global topology. However, in addition to the Global topology, other topologies are also developed in the related literature to enhance the performance of the PSO algorithm. The effects of PSO topologies depend significantly on the studied problems. Therefore, in this article, we conduct a performance investigation of eight PSO topologies in SDD. The success rate and mean iterations that are obtained from the numerical simulations are considered as the evaluation indexes. Furthermore, the average rank and Bonferroni-Dunn’s test are further utilized to perform the statistic analysis. From these analysis results, the Four Clusters are shown to be the more favorable PSO topologies in SDD.


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