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An improved particle swarm optimization algorithm for the reliability–redundancy allocation problem with global reliability

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  • Li, Shuai
  • Chi, Xuefen
  • Yu, Baozhu

Abstract

Most of the existing works on reliability–redundancy allocation problem (RRAP) focus on source-to-sink systems. This work formulates the RRAP with global reliability (g-reliability) called the g-reliability RRAP (gRRAP), which considers imperfect nodes and regards nodes and links of the system as subsystems. The g-reliability is the probability that all nodes of the system are connected through normal links, which is more suitable for the complex modern network systems featured by multi-sources, multi-sinks. We propose the algorithm to calculate the objective function of the gRRAP. And to solve the gRRAP, an improved particle swarm optimization algorithm with a specific particles initialization approach (called PIPSO) is developed. In the specific particles initialization approach, we propose a metric to evaluate the distance between two particles under the RRAP. The experimental results show that the PIPSO algorithm has better performance than several popular meta heuristic algorithms and previous works in solving the gRRAP and four benchmark problems. Most importantly, we propose a measure to evaluate the uniformity of particle position when particle swarm optimization is applied to RRAP. Results demonstrate that, the particle swarm optimization with more uniform initial particle distribution converges faster and better, and the obtained solutions fluctuate less.

Suggested Citation

  • Li, Shuai & Chi, Xuefen & Yu, Baozhu, 2022. "An improved particle swarm optimization algorithm for the reliability–redundancy allocation problem with global reliability," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002496
    DOI: 10.1016/j.ress.2022.108604
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    References listed on IDEAS

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    Cited by:

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    2. Mellal, Mohamed Arezki & Zio, Enrico & Al-Dahidi, Sameer & Masuyama, Naoki & Nojima, Yusuke, 2023. "System design optimization with mixed subsystems failure dependencies," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Yin, Mingang & Liu, Yu & Liu, Shuntao & Chen, Yiming & Yan, Yutao, 2023. "Scheduling heterogeneous repair channels in selective maintenance of multi-state systems with maintenance duration uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Chowdury, Md. Abdul Malek & Nath, Rahul & Shukla, Amit K. & Rauniyar, Amit & Muhuri, Pranab K., 2024. "Multi-task optimization in reliability redundancy allocation problem: A multifactorial evolutionary-based approach," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Yeh, Wei-Chang, 2022. "BAT-based algorithm for finding all Pareto solutions of the series-parallel redundancy allocation problem with mixed components," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Hsieh, Tsung-Jung, 2023. "A Q-learning guided search for developing a hybrid of mixed redundancy strategies to improve system reliability," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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