A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties
As engineering systems become increasingly complex, reliability-based design optimization (RBDO) has been extensively studied in recent years and has made great progress. In order to achieve better optimization results, the mathematical model used needs to consider a large number of uncertain factor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/23/4790 |
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| author | Shiyuan Yang Hongtao Wang Yihe Xu Yongqiang Guo Lidong Pan Jiaming Zhang Xinkai Guo Debiao Meng Jiapeng Wang |
| author_facet | Shiyuan Yang Hongtao Wang Yihe Xu Yongqiang Guo Lidong Pan Jiaming Zhang Xinkai Guo Debiao Meng Jiapeng Wang |
| author_sort | Shiyuan Yang |
| collection | DOAJ |
| description | As engineering systems become increasingly complex, reliability-based design optimization (RBDO) has been extensively studied in recent years and has made great progress. In order to achieve better optimization results, the mathematical model used needs to consider a large number of uncertain factors. Especially when considering mixed uncertainty factors, the contradiction between the large computational cost and the efficiency of the optimization algorithm becomes increasingly fierce. How to quickly find the optimal most probable point (MPP) will be an important research direction of RBDO. To solve this problem, this paper constructs a new RBDO method framework by combining an improved particle swarm algorithm (PSO) with excellent global optimization capabilities and a decoupling strategy using a simulated annealing algorithm (SA). This study improves the efficiency of the RBDO solution by quickly solving MPP points and decoupling optimization strategies. At the same time, the accuracy of RBDO results is ensured by enhancing global optimization capabilities. Finally, this article illustrates the superiority and feasibility of this method through three calculation examples. |
| first_indexed | 2024-03-09T01:47:18Z |
| format | Article |
| id | doaj.art-c10db179d13f47f794a46cd34e05d19c |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T01:47:18Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c10db179d13f47f794a46cd34e05d19c2023-12-08T15:21:46ZengMDPI AGMathematics2227-73902023-11-011123479010.3390/math11234790A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid UncertaintiesShiyuan Yang0Hongtao Wang1Yihe Xu2Yongqiang Guo3Lidong Pan4Jiaming Zhang5Xinkai Guo6Debiao Meng7Jiapeng Wang8School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaGlasgow College, University of Electronic Science and Technology of China, Chengdu 611731, ChinaBeijing Research Institute of Mechanical & Electrical Technology Ltd., Beijing 100083, ChinaBeijing Research Institute of Mechanical & Electrical Technology Ltd., Beijing 100083, ChinaBeijing Research Institute of Mechanical & Electrical Technology Ltd., Beijing 100083, ChinaInstitute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan 523808, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaAs engineering systems become increasingly complex, reliability-based design optimization (RBDO) has been extensively studied in recent years and has made great progress. In order to achieve better optimization results, the mathematical model used needs to consider a large number of uncertain factors. Especially when considering mixed uncertainty factors, the contradiction between the large computational cost and the efficiency of the optimization algorithm becomes increasingly fierce. How to quickly find the optimal most probable point (MPP) will be an important research direction of RBDO. To solve this problem, this paper constructs a new RBDO method framework by combining an improved particle swarm algorithm (PSO) with excellent global optimization capabilities and a decoupling strategy using a simulated annealing algorithm (SA). This study improves the efficiency of the RBDO solution by quickly solving MPP points and decoupling optimization strategies. At the same time, the accuracy of RBDO results is ensured by enhancing global optimization capabilities. Finally, this article illustrates the superiority and feasibility of this method through three calculation examples.https://www.mdpi.com/2227-7390/11/23/4790reliability-based design and optimizationparticle swarm optimization algorithmsimulated annealing algorithmmost probable point |
| spellingShingle | Shiyuan Yang Hongtao Wang Yihe Xu Yongqiang Guo Lidong Pan Jiaming Zhang Xinkai Guo Debiao Meng Jiapeng Wang A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties Mathematics reliability-based design and optimization particle swarm optimization algorithm simulated annealing algorithm most probable point |
| title | A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties |
| title_full | A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties |
| title_fullStr | A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties |
| title_full_unstemmed | A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties |
| title_short | A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties |
| title_sort | coupled simulated annealing and particle swarm optimization reliability based design optimization strategy under hybrid uncertainties |
| topic | reliability-based design and optimization particle swarm optimization algorithm simulated annealing algorithm most probable point |
| url | https://www.mdpi.com/2227-7390/11/23/4790 |
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