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Reliability Optimization of Hybrid Systems Driven by Constraint Importance Measure Considering Different Cost Functions

Author

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  • Jiangbin Zhao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an 710054, China)

  • Mengtao Liang

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an 710054, China)

  • Rongyu Tian

    (Hongfujin Precision Electronics (Chengdu) Co., Ltd., Chengdu 611730, China)

  • Zaoyan Zhang

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an 710054, China)

  • Xiangang Cao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi’an 710054, China)

Abstract

The requirements of high reliability for hybrid systems are urgent for engineers to maximize the system reliability under the limited cost budget. The cost constraint importance measure (CIM) is an important tool to achieve the local optimal solution by considering the relationship between constraint conditions and objective functions in the optimization problem. To better consider the contribution of the CIM, this paper considers three different cost function forms, including power type, trigonometric type, and exponential type. Combining the global search ability of the arithmetic optimization algorithm (AOA) with the local search ability of the CIM, a CIM-based arithmetic optimization algorithm (CIAOA) is developed to analyze the contribution of the CIM. Through the numerical experiments, the optimal system reliability and convergence generation of the CIAOA and AOA under different cost function forms are regarded as the indexes to analyze algorithm performance. The experimental results show that the average system reliability improvement percentages under power type, trigonometric type, and exponential cost constraint are 8.07%, 0.14%, and 0.53%, respectively, while the average convergence improvement percentages under three cost forms are 37.30%, 0.08%, and 1.66%, respectively. Therefore, the CIAOA performs the best under power cost constraints. Finally, a numerical example of a hybrid power vehicle system is introduced to analyze the contribution of the CIM under different cost functions by considering the reliability improvement rate in the optimal solution and the ranking of the CIM. The higher prioritization components in the two rankings are similar, which shows that the component with higher a CIM is selected to improve its reliability.

Suggested Citation

  • Jiangbin Zhao & Mengtao Liang & Rongyu Tian & Zaoyan Zhang & Xiangang Cao, 2023. "Reliability Optimization of Hybrid Systems Driven by Constraint Importance Measure Considering Different Cost Functions," Mathematics, MDPI, vol. 11(20), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4283-:d:1259528
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    References listed on IDEAS

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    1. Cai, Zhiqiang & Si, Shubin & Sun, Shudong & Li, Caitao, 2016. "Optimization of linear consecutive-k-out-of-n system with a Birnbaum importance-based genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 248-258.
    2. Napat Harnpornchai & Wiriyaporn Wonggattaleekam, 2021. "An Application of Neutrosophic Set to Relative Importance Assignment in AHP," Mathematics, MDPI, vol. 9(20), pages 1-14, October.
    3. Soni Bisht & Akshay Kumar & Nupur Goyal & Mangey Ram & Yury Klochkov, 2021. "Analysis of Network Reliability Characteristics and Importance of Components in a Communication Network," Mathematics, MDPI, vol. 9(12), pages 1-13, June.
    4. Shengkun Xie & Rebecca Luo, 2022. "Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions," Mathematics, MDPI, vol. 10(10), pages 1-19, May.
    5. Si, Shubin & Levitin, Gregory & Dui, Hongyan & Sun, Shudong, 2013. "Component state-based integrated importance measure for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 75-83.
    6. Dui, Hongyan & Tian, Tianzi & Zhao, Jiangbin & Wu, Shaomin, 2022. "Comparing with the joint importance under consideration of consecutive-k-out-of-n system structure changes," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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    Cited by:

    1. Majid Forghani-elahabad & Omar Mutab Alsalami, 2023. "Using a Node–Child Matrix to Address the Quickest Path Problem in Multistate Flow Networks under Transmission Cost Constraints," Mathematics, MDPI, vol. 11(24), pages 1-15, December.

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