Bridge Critical State Search by Using Quantum Genetic Firefly Algorithm
When performing flutter analysis through the traditional methods, it is difficult to solve high-order strong nonlinear equations. For overcoming this difficulty, this paper establishes a double-parameter optimization model for searching the flutter critical wind speed and frequency. A new hybrid fir...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/3253280 |
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| _version_ | 1849396413386981376 |
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| author | Shi-bo Tao Dian-zhong Liu Ai-ping Tang |
| author_facet | Shi-bo Tao Dian-zhong Liu Ai-ping Tang |
| author_sort | Shi-bo Tao |
| collection | DOAJ |
| description | When performing flutter analysis through the traditional methods, it is difficult to solve high-order strong nonlinear equations. For overcoming this difficulty, this paper establishes a double-parameter optimization model for searching the flutter critical wind speed and frequency. A new hybrid firefly algorithm called the quantum genetic firefly algorithm is presented to search the optimal solution to the optimization model. The proposed algorithm is the combination of the firefly algorithm and the quantum genetic algorithm. The results of the quantum genetic firefly algorithm are compared with the results shown by the firefly algorithm and quantum genetic algorithm. Numerical and experimental results of the proposed algorithm are competitive and in most cases are better than that of the firefly algorithm and quantum genetic algorithm. |
| format | Article |
| id | doaj-art-b2bc488602df4541a4a4f80fdc0c4e6a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-b2bc488602df4541a4a4f80fdc0c4e6a2025-08-20T03:39:21ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/32532803253280Bridge Critical State Search by Using Quantum Genetic Firefly AlgorithmShi-bo Tao0Dian-zhong Liu1Ai-ping Tang2School of Civil Engineering, Jilin Jianzhu University, Changchun, Jilin 130118, ChinaSchool of Civil Engineering, Jilin Jianzhu University, Changchun, Jilin 130118, ChinaKey Lab of Structures Dynamic Behavior and Control, Harbin Institute of Technology, Ministry of Education, Heilongjiang, Harbin 150090, ChinaWhen performing flutter analysis through the traditional methods, it is difficult to solve high-order strong nonlinear equations. For overcoming this difficulty, this paper establishes a double-parameter optimization model for searching the flutter critical wind speed and frequency. A new hybrid firefly algorithm called the quantum genetic firefly algorithm is presented to search the optimal solution to the optimization model. The proposed algorithm is the combination of the firefly algorithm and the quantum genetic algorithm. The results of the quantum genetic firefly algorithm are compared with the results shown by the firefly algorithm and quantum genetic algorithm. Numerical and experimental results of the proposed algorithm are competitive and in most cases are better than that of the firefly algorithm and quantum genetic algorithm.http://dx.doi.org/10.1155/2019/3253280 |
| spellingShingle | Shi-bo Tao Dian-zhong Liu Ai-ping Tang Bridge Critical State Search by Using Quantum Genetic Firefly Algorithm Shock and Vibration |
| title | Bridge Critical State Search by Using Quantum Genetic Firefly Algorithm |
| title_full | Bridge Critical State Search by Using Quantum Genetic Firefly Algorithm |
| title_fullStr | Bridge Critical State Search by Using Quantum Genetic Firefly Algorithm |
| title_full_unstemmed | Bridge Critical State Search by Using Quantum Genetic Firefly Algorithm |
| title_short | Bridge Critical State Search by Using Quantum Genetic Firefly Algorithm |
| title_sort | bridge critical state search by using quantum genetic firefly algorithm |
| url | http://dx.doi.org/10.1155/2019/3253280 |
| work_keys_str_mv | AT shibotao bridgecriticalstatesearchbyusingquantumgeneticfireflyalgorithm AT dianzhongliu bridgecriticalstatesearchbyusingquantumgeneticfireflyalgorithm AT aipingtang bridgecriticalstatesearchbyusingquantumgeneticfireflyalgorithm |