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Using particle-based simplified swarm optimization to solve the cold-standby reliability of the gas turbine industry

Author

Listed:
  • Shakuntla Singla

    (MMEC, Maharishi Markandeshwar (Deemed to Be University))

  • Komalpreet Kaur

    (MMEC, Maharishi Markandeshwar (Deemed to Be University))

Abstract

Simplified swarm optimization (SSO) and particle swarm optimization (PSO) are two types of modern swarm intelligence techniques that are often used for optimization. In order to identify the most effective system RRAP with a cold-standby strategic plan while aiming to exploit the reliability of the organization, the article discusses a PSSO procedure that combines UM of PSO and Simplified swarm optimization, PSSO is especially impressive in comparison with other recently incorporated algorithms into four popular applications, namely a sequences scheme, a complex organization, a series–parallel system, and an airspeed indicator defense system for a turbine, with extensive experiments conducted on the pretty standard and well-known four benchmarks of reliability-redundancy allocation problems. Finally, the experiment findings show that the particle-based simplified swarm optimization can successfully solution to address the reliability-redundancy allocation (RRAP) issues using the cold-standby method and performs well in terms of organization reliability, even though the best platform consistency is not attained in all four benchmarks and experiment is done using python and Google colab.

Suggested Citation

  • Shakuntla Singla & Komalpreet Kaur, 2024. "Using particle-based simplified swarm optimization to solve the cold-standby reliability of the gas turbine industry," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4456-4465, September.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:9:d:10.1007_s13198-024-02457-x
    DOI: 10.1007/s13198-024-02457-x
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    References listed on IDEAS

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    1. Kim, Heungseob & Kim, Pansoo, 2017. "Reliability–redundancy allocation problem considering optimal redundancy strategy using parallel genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 153-160.
    2. Deepika Garg & Sarita Devi, 2021. "RAP via hybrid genetic simulating annealing algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(3), pages 419-425, June.
    3. Sarita Devi & Deepika Garg, 2020. "Hybrid genetic and particle swarm algorithm: redundancy allocation problem," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 313-319, April.
    4. Xianyong Zhang & Wei-chang Yeh & Yunzhi Jiang & Yaohong Huang & Yingwang Xiao & Li Li, 2018. "A Case Study of Control and Improved Simplified Swarm Optimization for Economic Dispatch of a Stand-Alone Modular Microgrid," Energies, MDPI, vol. 11(4), pages 1-21, March.
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