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

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  • Shiyuan Yang

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan 523808, China)

  • Hongtao Wang

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan 523808, 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
    Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan 523808, China)

  • Jiapeng Wang

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

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.

Suggested Citation

  • Shiyuan Yang & Hongtao Wang & Yihe Xu & Yongqiang Guo & Lidong Pan & Jiaming Zhang & Xinkai Guo & Debiao Meng & Jiapeng Wang, 2023. "A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties," Mathematics, MDPI, vol. 11(23), pages 1-26, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:23:p:4790-:d:1288871
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    References listed on IDEAS

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    1. Yun Tan & Changshu Zhan & Youchun Pi & Chunhui Zhang & Jinghui Song & Yan Chen & Amir-Mohammad Golmohammadi, 2023. "A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines," Mathematics, MDPI, vol. 11(10), pages 1-18, May.
    2. Umberto Bartoccini & Arturo Carpi & Valentina Poggioni & Valentino Santucci, 2019. "Memes Evolution in a Memetic Variant of Particle Swarm Optimization," Mathematics, MDPI, vol. 7(5), pages 1-13, May.
    3. Meng, Debiao & Yang, Shiyuan & Jesus, Abílio M.P. de & Zhu, Shun-Peng, 2023. "A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower," Renewable Energy, Elsevier, vol. 203(C), pages 407-420.
    4. Leonid Plotnikov, 2023. "Preparation and Analysis of Experimental Findings on the Thermal and Mechanical Characteristics of Pulsating Gas Flows in the Intake System of a Piston Engine for Modelling and Machine Learning," Mathematics, MDPI, vol. 11(8), pages 1-16, April.
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