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Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0003-3794-979X
To locate the damage of the structure efficiently and judge the damage degree, this paper proposes an improved fruit fly optimization algorithm (IFOA). Aiming at the problem of poor convergence of the standard fruit fly optimization algorithm in the face of complex structure damage identification, the IFOA introduces the concept of collaborative search of two subpopulations. The IFOA divides the entire population into positive subgroups and negative subgroups based on the individual taste concentration results. Among them, the positive subgroup uses the improved dynamic adaptive search step size to perform a fine search locally to improve its local search ability. Negative subgroups continue to use the standard fruit fly optimization algorithm for optimization, taking advantage of the powerful global search capabilities of the standard fruit fly optimization algorithm. It enables the algorithm to balance global and local search capabilities, prevents the algorithm from falling into local optimum, and speeds up the convergence speed and accuracy of the algorithm. Simulation results show that IFOA can effectively identify the damage location and damage degree of the structure, and it still performs well when facing the complex steel truss damage identification.
Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0003-3794-979X
To locate the damage of the structure efficiently and judge the damage degree, this paper proposes an improved fruit fly optimization algorithm (IFOA). Aiming at the problem of poor convergence of the standard fruit fly optimization algorithm in the face of complex structure damage identification, the IFOA introduces the concept of collaborative search of two subpopulations. The IFOA divides the entire population into positive subgroups and negative subgroups based on the individual taste concentration results. Among them, the positive subgroup uses the improved dynamic adaptive search step size to perform a fine search locally to improve its local search ability. Negative subgroups continue to use the standard fruit fly optimization algorithm for optimization, taking advantage of the powerful global search capabilities of the standard fruit fly optimization algorithm. It enables the algorithm to balance global and local search capabilities, prevents the algorithm from falling into local optimum, and speeds up the convergence speed and accuracy of the algorithm. Simulation results show that IFOA can effectively identify the damage location and damage degree of the structure, and it still performs well when facing the complex steel truss damage identification.
Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0003-3794-979X
To locate the damage of the structure efficiently and judge the damage degree, this paper proposes an improved fruit fly optimization algorithm (IFOA). Aiming at the problem of poor convergence of the standard fruit fly optimization algorithm in the face of complex structure damage identification, the IFOA introduces the concept of collaborative search of two subpopulations. The IFOA divides the entire population into positive subgroups and negative subgroups based on the individual taste concentration results. Among them, the positive subgroup uses the improved dynamic adaptive search step size to perform a fine search locally to improve its local search ability. Negative subgroups continue to use the standard fruit fly optimization algorithm for optimization, taking advantage of the powerful global search capabilities of the standard fruit fly optimization algorithm. It enables the algorithm to balance global and local search capabilities, prevents the algorithm from falling into local optimum, and speeds up the convergence speed and accuracy of the algorithm. Simulation results show that IFOA can effectively identify the damage location and damage degree of the structure, and it still performs well when facing the complex steel truss damage identification.
Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm
KSCE J Civ Eng
Xiong, Chunbao (Autor:in) / Lian, Sida (Autor:in)
KSCE Journal of Civil Engineering ; 25 ; 985-1007
01.03.2021
23 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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