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Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface

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  • Li, Junxiang
  • Chen, Jianqiao

Abstract

It remains a great challenge to investigate time-variant reliability-based design optimization (TRBDO) problems, owing to the high computational cost of time-variant reliability analysis, and the difficulties in modeling. To address these issues, PSO-t-IRS, a methodology incorporating a particle swarm optimization (PSO) algorithm and an enhanced instantaneous response surface (t-IRS), is proposed. First, the enhanced t-IRS method is developed to construct the extended instantaneous response surrogate model. It is worth noting that, different from the original t-IRS method, the design variables are added to the input parameters in constructing the surrogate model. When generating Monte Carlo samples for reliability calculation, the design variables and the time parameter are treated as uniformly distributed random variables. The extended surrogate model construction is the key factor for solving the TRBDO. Once the model is built, time-variant reliability can be computed conveniently, and the solution of TRBDO is readily found by using any efficient optimization algorithm. In this work, the extended surrogate model is integrated into the PSO algorithm, leading to the proposed method, PSO-t-IRS. The effectiveness of the PSO-t-IRS method is demonstrated with two TRBDO examples. Thereinafter, the proposed method is applied to four system TRBDO examples featuring series, parallel, compound systems and a 23-bar truss problem.

Suggested Citation

  • Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s0951832018306008
    DOI: 10.1016/j.ress.2019.106580
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    8. Zhao, Zhao & Zhao, Yan-Gang & Li, Pei-Pei, 2023. "A novel decoupled time-variant reliability-based design optimization approach by improved extreme value moment method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    9. Jiang, Zhiyuan & Huang, Xianzhen & Wang, Bingxiang & Liao, Xin & Liu, Huizhen & Ding, Pengfei, 2024. "Time-dependent reliability-based design optimization of main shaft bearings in wind turbines involving mixed-integer variables," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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