Wind resistance performance optimization of PSO algorithm in skyscrapers design
To optimize the skyscrapers and enhance its wind resistance performance under wind load, a wind resistance optimization method for super high-rise building structure on the basis of improved Particle Swarm Optimization (PSO) is built. Through the harmonic excitation method, the equivalent static win...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | Sustainable Buildings |
| Subjects: | |
| Online Access: | https://www.sustainable-buildings-journal.org/articles/sbuild/full_html/2025/01/sbuild20240034/sbuild20240034.html |
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| Summary: | To optimize the skyscrapers and enhance its wind resistance performance under wind load, a wind resistance optimization method for super high-rise building structure on the basis of improved Particle Swarm Optimization (PSO) is built. Through the harmonic excitation method, the equivalent static wind load is calculated, and the desired mathematical model is constructed. The chaotic mapping technology is introduced and the chaotic local search strategy is adopted to further accurately optimize the update process of the global optimal solution. The PSO converged rapidly in the first 50 iterations. The objective function value decreased from about 0.45 to less than 0.1, and then the decline rate slowed down and tended to be stable. After 150 iterations, it was basically stable and close to 0, indicating that the optimal solution was found. The initial target value of quantum-behaved PSO was about 0.5, which decreased rapidly in the first 50 times, and slowly optimized from 50 to 200 times, and then tended to be stable and close to 0 after 200 times. The algorithm converged rapidly in the first 50 times, and had good stability after 200 times, which could find a better solution. The displacement and stress of the optimized structure under wind load meet the specification, the local search efficiency of quantum-behaved PSO is higher, and the global search ability is enhanced by chaotic mapping. |
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| ISSN: | 2492-6035 |