A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties

Shiyuan Yang, Hongtao Wang, Yihe Xu, Yongqiang Guo, Lidong Pan, Jiaming Zhang, Xinkai Guo, Debiao Meng () and Jiapeng Wang
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Shiyuan Yang: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Hongtao Wang: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Yihe Xu: Glasgow College, University of Electronic Science and Technology of China, Chengdu 611731, China
Yongqiang Guo: Beijing Research Institute of Mechanical & Electrical Technology Ltd., Beijing 100083, China
Lidong Pan: Beijing Research Institute of Mechanical & Electrical Technology Ltd., Beijing 100083, China
Jiaming Zhang: Beijing Research Institute of Mechanical & Electrical Technology Ltd., Beijing 100083, China
Xinkai Guo: Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan 523808, China
Debiao Meng: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Jiapeng Wang: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Mathematics, 2023, vol. 11, issue 23, 1-26

Abstract: 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.

Keywords: reliability-based design and optimization; particle swarm optimization algorithm; simulated annealing algorithm; most probable point (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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