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A Multistrategy Fusion-Improved Black Widow Optimization Algorithm for Structural Damage Identification
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DOI：10.1155/stc/2939779.png)
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Abstract
En 中文
Structural damage identification based on metaheuristic algorithms is an important part of structural health monitoring with great potential. However, the metaheuristic intelligent algorithms probably have flaws of slow convergence speed and low calculation accuracy, which need to be improved to address engineering optimization problems. In this paper, the black widow optimization (BWO) algorithm is used for structural damage identification. In addition, a multistrategy fusion-improved BWO (IBWO) algorithm is proposed by introducing the tent chaotic mapping, the golden sine equation, the gazelle wandering equation, and the boundary treatments. First, in the population initialization stage, tent chaotic mapping is introduced to improve the quality of the initial solution. Second, the golden sine strategy is used to acquire the optimal solution quickly in local search. Then, the motion equation of the gazelle algorithm is employed to enhance the global search ability and avoid the algorithm falling into the local optimal solution. Finally, the boundary processing strategy is presented to reduce the calculation of solutions and improve the optimization efficiency. A novel damage identification objective function is redefined by combining the modal assurance criterion and the modal flexibility. Then, a two-story rigid frame structure is utilized for numerical simulations. Moreover, experimental studies with a simply supported beam were carried out to verify the performance of the proposed damage identification method. Simulation results and experimental studies demonstrate that, even with the interference of strong noise, the IBWO algorithm has a higher accuracy and efficiency in damage identification compared to the BWO algorithm.
Keywords:
black widow optimization algorithm
experimental studies
golden sine equation
structural health monitoring
tent chaotic mapping
Journal
IF:
5.1
Papers:
242
Citations:
9.2K