Parameter Selection for PSO-Based Hybrid Algorithms and Its Effect on Crack Detection in Cantilever Beams
The importance of the parameters of any optimization algorithm, especially meta-heuristic algorithms that have been created to simplify the solution of optimization problems, is inevitable. The optimal values of these parameters, which generally depend on the specifics of the problem in question, ha...
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| Format: | Article |
| Language: | English |
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K. N. Toosi University of Technology
2024-11-01
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| Series: | Numerical Methods in Civil Engineering |
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| Online Access: | https://nmce.kntu.ac.ir/article_209075_bdf1ae3f83a9a28c5367b790c78cbe2f.pdf |
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| _version_ | 1832590734049411072 |
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| author | Amin Ghannadiasl Saeedeh Ghaemifard |
| author_facet | Amin Ghannadiasl Saeedeh Ghaemifard |
| author_sort | Amin Ghannadiasl |
| collection | DOAJ |
| description | The importance of the parameters of any optimization algorithm, especially meta-heuristic algorithms that have been created to simplify the solution of optimization problems, is inevitable. The optimal values of these parameters, which generally depend on the specifics of the problem in question, have a significant impact on the performance of the mentioned algorithms and a better search of the solution space. Parameters selection of them will play an important role in performance and efficiency of the algorithms. This article examines the capability of various optimization algorithms and suggests dual hybrid optimization algorithms are named PSO-FA, PSO-GA, PSO-GWO, for solving the problem of computing the depth and location of cracks in cantilever beams. The performance of Particle swarm optimization (PSO), Genetic algorithm (GA), Grey wolf optimization (GWO), Firefly algorithm (FA), and hybrid of them base on PSO optimizer to determine the location and depth of crack for cantilever beam are proposed. These suggested algorithms are optimization algorithms based on intelligent optimization. So, the performance of these algorithms are analyzed when the control parameters vary. |
| format | Article |
| id | doaj-art-32d9fc5e461f44b0a2415ed13da5a973 |
| institution | Kabale University |
| issn | 2345-4296 2783-3941 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | K. N. Toosi University of Technology |
| record_format | Article |
| series | Numerical Methods in Civil Engineering |
| spelling | doaj-art-32d9fc5e461f44b0a2415ed13da5a9732025-01-23T08:03:13ZengK. N. Toosi University of TechnologyNumerical Methods in Civil Engineering2345-42962783-39412024-11-0192172810.61186/NMCE.2311.1036209075Parameter Selection for PSO-Based Hybrid Algorithms and Its Effect on Crack Detection in Cantilever BeamsAmin Ghannadiasl0Saeedeh Ghaemifard1Associate Professor, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, IranPh.D. student, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.The importance of the parameters of any optimization algorithm, especially meta-heuristic algorithms that have been created to simplify the solution of optimization problems, is inevitable. The optimal values of these parameters, which generally depend on the specifics of the problem in question, have a significant impact on the performance of the mentioned algorithms and a better search of the solution space. Parameters selection of them will play an important role in performance and efficiency of the algorithms. This article examines the capability of various optimization algorithms and suggests dual hybrid optimization algorithms are named PSO-FA, PSO-GA, PSO-GWO, for solving the problem of computing the depth and location of cracks in cantilever beams. The performance of Particle swarm optimization (PSO), Genetic algorithm (GA), Grey wolf optimization (GWO), Firefly algorithm (FA), and hybrid of them base on PSO optimizer to determine the location and depth of crack for cantilever beam are proposed. These suggested algorithms are optimization algorithms based on intelligent optimization. So, the performance of these algorithms are analyzed when the control parameters vary.https://nmce.kntu.ac.ir/article_209075_bdf1ae3f83a9a28c5367b790c78cbe2f.pdfcrack detectioncantilever beamhybrid algorithmparameters selection of algorithmsparticle swarm |
| spellingShingle | Amin Ghannadiasl Saeedeh Ghaemifard Parameter Selection for PSO-Based Hybrid Algorithms and Its Effect on Crack Detection in Cantilever Beams Numerical Methods in Civil Engineering crack detection cantilever beam hybrid algorithm parameters selection of algorithms particle swarm |
| title | Parameter Selection for PSO-Based Hybrid Algorithms and Its Effect on Crack Detection in Cantilever Beams |
| title_full | Parameter Selection for PSO-Based Hybrid Algorithms and Its Effect on Crack Detection in Cantilever Beams |
| title_fullStr | Parameter Selection for PSO-Based Hybrid Algorithms and Its Effect on Crack Detection in Cantilever Beams |
| title_full_unstemmed | Parameter Selection for PSO-Based Hybrid Algorithms and Its Effect on Crack Detection in Cantilever Beams |
| title_short | Parameter Selection for PSO-Based Hybrid Algorithms and Its Effect on Crack Detection in Cantilever Beams |
| title_sort | parameter selection for pso based hybrid algorithms and its effect on crack detection in cantilever beams |
| topic | crack detection cantilever beam hybrid algorithm parameters selection of algorithms particle swarm |
| url | https://nmce.kntu.ac.ir/article_209075_bdf1ae3f83a9a28c5367b790c78cbe2f.pdf |
| work_keys_str_mv | AT aminghannadiasl parameterselectionforpsobasedhybridalgorithmsanditseffectoncrackdetectionincantileverbeams AT saeedehghaemifard parameterselectionforpsobasedhybridalgorithmsanditseffectoncrackdetectionincantileverbeams |