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NSGA-II based fuzzy multi-objective reliability analysis

Author

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  • Hemant Kumar

    (Indian Institute of Technology Roorkee)

  • Shiv Prasad Yadav

    (Indian Institute of Technology Roorkee)

Abstract

In many practical situations, we need to reduce the cost of the system and improve its reliability simultaneously. At the same time, all the design data involved in the system design are not precisely known. Incompleteness and unreliability of input information are typical for many practical problems in the multi-objective optimization of system design. In this work, we have analyzed fuzzy multi-objective optimization problem of main characteristics of system design such as reliability and cost simultaneously based on non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is one of the multi-objective evolutionary algorithms (MOEAs), provides the decision-maker with a complete picture of the Pareto-optimal solutions space. It finds increasing applications in solving the multi-objective optimization problem (MOOP) because of low computational requirements, elitism, and parameter-less sharing approach. A brief description of NSGA-II and its use for MOOP is given. We have obtained multiple solutions (Pareto-optimal solutions) in a single simulation run. A numerical example of a series system is given to illustrate the proposed approach.

Suggested Citation

  • Hemant Kumar & Shiv Prasad Yadav, 2017. "NSGA-II based fuzzy multi-objective reliability analysis," 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. 8(4), pages 817-825, December.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:4:d:10.1007_s13198-017-0672-y
    DOI: 10.1007/s13198-017-0672-y
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    References listed on IDEAS

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    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Salazar, Daniel & Rocco, Claudio M. & Galván, Blas J., 2006. "Optimization of constrained multiple-objective reliability problems using evolutionary algorithms," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1057-1070.
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    Cited by:

    1. Shadmani, Alireza & Nikoo, Mohammad Reza & Gandomi, Amir H., 2024. "Adaptive systematic optimization of a multi-axis ocean wave energy converter," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Hemant Kumar & Shiv Prasad Yadav, 2019. "Fuzzy rule-based reliability analysis using NSGA-II," 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. 10(5), pages 953-972, October.
    3. Hemant Kumar & R. N. Prajapati, 2023. "Coordination analysis of system reliability using NSGA-II: a comparative study," 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. 14(6), pages 2514-2526, December.
    4. Chenyang Ma & Wei Wang & Zhiqiang Cai & Jiangbin Zhao, 2022. "Maintenance optimization of reconfigurable systems based on multi-objective Birnbaum importance," Journal of Risk and Reliability, , vol. 236(2), pages 277-289, April.

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