.png){kind=icon}
.png){kind=icon}
Return
Structural identification using improved butterfly optimization algorithm with adaptive sampling test and search space reduction method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.istruc.2021.05.043.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Swarm intelligence optimization algorithms have been widely used in structural identification due to its powerful search ability, while an unpleasant identification result is regularly achieved with predefined wide search range. To address this issue, a hybrid strategy, initially reducing search space by adaptive sampling test and then identifying the actual structural parameters with improved butterfly optimization algorithm (IBOA) in the reduced search range by adaptive search space reduction method, is proposed and employed in this paper. In one aspect, the clustering competition learning mechanism and the chaotic elite learning mechanism are introduced to improve the performance of butterfly optimization algorithm (BOA). In the other aspect, adaptive sampling test and search space reduction method are also developed for search space reduction of unknown parameters. Genetic algorithm, BOA, IBOA, and proposed hybrid sampling and IBOA (sampling-IBOA) are evaluated by numerical examples of simply-supported beam and truss structure, as well as an experimental test of steel grid benchmark structure for comparative study. In addition, the effect of the window width, four different sampling methods, hybrid Ham-IBOA and gradient search on application of the proposed sampling-IBOA method are further illustrated. The numerical and experimental results demonstrate that the proposed sampling-IBOA method can significantly improve computational efficiency and identification accuracy, especially for Hammersley sequence sampling among the four sampling methods.
Keywords:
Structural identification
Sampling methods
Swarm intelligence methods
Improved butterfly optimization algorithm
Hammersley sequence sampling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.3
Papers:
1.2W
Citations:
2.7W