IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v222y2008i2p115-126.html
   My bibliography  Save this article

Fusion of artificial neural networks and genetic algorithms for multi-objective system reliability design optimization

Author

Listed:
  • E Zio
  • F Di Maio
  • S Martorell

Abstract

In this work, artificial neural networks (ANNs) are used to include the decision-maker's preference structure within a genetic algorithm (GA) search of the optimal system reliability configuration. For verification, the proposed approach is applied to two literature case studies of increasing complexity concerning the optimization of the reliability design of a series system.

Suggested Citation

  • E Zio & F Di Maio & S Martorell, 2008. "Fusion of artificial neural networks and genetic algorithms for multi-objective system reliability design optimization," Journal of Risk and Reliability, , vol. 222(2), pages 115-126, June.
    • Handle: RePEc:sae:risrel:v:222:y:2008:i:2:p:115-126
    DOI: 10.1243/1748006XJRR126
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1243/1748006XJRR126
    Download Restriction: no
    File URL: https://libkey.io/10.1243/1748006XJRR126?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Marseguerra, M. & Zio, E. & Martorell, S., 2006. "Basics of genetic algorithms optimization for RAMS applications," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 977-991.
    2. Sakawa, Masatoshi, 1982. "Interactive multiobjective decision making by the sequential proxy optimization technique: SPOT," European Journal of Operational Research, Elsevier, vol. 9(4), pages 386-396, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M. K. Pandey & M. K. Tiwari & M. J. Zuo, 2007. "Interactive enhanced particle swarm optimization: A multi-objective reliability application," Journal of Risk and Reliability, , vol. 221(3), pages 177-191, September.
    2. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    3. Peiravi, Abdossaber & Nourelfath, Mustapha & Zanjani, Masoumeh Kazemi, 2022. "Universal redundancy strategy for system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Cook, Jason L. & Ramirez-Marquez, Jose Emmanuel, 2009. "Optimal design of cluster-based ad-hoc networks using probabilistic solution discovery," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 218-228.
    5. Zio, E. & Pedroni, N., 2010. "An optimized Line Sampling method for the estimation of the failure probability of nuclear passive systems," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1300-1313.
    6. Mariano Luque & Rafael Caballero & Julian Molina & Francisco Ruiz, 2007. "Equivalent Information for Multiobjective Interactive Procedures," Management Science, INFORMS, vol. 53(1), pages 125-134, January.
    7. Mostafa Kazemi & Zakieh Shooshtarian, 2013. "Surrogate Worth Trade Off Method’S Review, Applications And Extension," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 8(1), pages 99-109, December.
    8. L Podofillini & E Zio, 2008. "Events group risk importance by genetic algorithms," Journal of Risk and Reliability, , vol. 222(3), pages 337-346, September.
    9. Anil Kr. Aggarwal & Vikram Singh & Sanjeev Kumar, 2017. "Availability analysis and performance optimization of a butter oil production system: a case 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. 8(1), pages 538-554, January.
    10. Compare, Michele & Bellani, Luca & Zio, Enrico, 2019. "Optimal allocation of prognostics and health management capabilities to improve the reliability of a power transmission network," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 164-180.
    11. Kaliszewski, Ignacy, 2004. "Out of the mist--towards decision-maker-friendly multiple criteria decision making support," European Journal of Operational Research, Elsevier, vol. 158(2), pages 293-307, October.
    12. Okafor, Ekene Gabriel & Sun, You-Chao, 2012. "Multi-objective optimization of a series–parallel system using GPSIA," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 61-71.
    13. Mellal, Mohamed Arezki & Al-Dahidi, Sameer & Williams, Edward J., 2020. "System reliability optimization with heterogeneous components using hosted cuckoo optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    14. Miettinen, Kaisa & Makela, Marko M. & Kaario, Katja, 2006. "Experiments with classification-based scalarizing functions in interactive multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 175(2), pages 931-947, December.
    15. Longhi, Antonio Eduardo Bier & Pessoa, Artur Alves & Garcia, Pauli Adriano de Almada, 2015. "Multiobjective optimization of strategies for operation and testing of low-demand safety instrumented systems using a genetic algorithm and fault trees," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 525-538.
    16. Edoardo Patelli & Gerhart Schuëller, 2012. "Computational optimization strategies for the simulation of random media and components," Computational Optimization and Applications, Springer, vol. 53(3), pages 903-931, December.
    17. Rafael Lazimy, 2013. "Interactive Polyhedral Outer Approximation (IPOA) strategy for general multiobjective optimization problems," Annals of Operations Research, Springer, vol. 210(1), pages 73-99, November.
    18. Zio, E. & Baraldi, P. & Pedroni, N., 2009. "Optimal power system generation scheduling by multi-objective genetic algorithms with preferences," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 432-444.
    19. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2009. "Modelling and optimization of proof testing policies for safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 838-854.
    20. Di Maio, Francesco & Marchetti, Stefano & Zio, Enrico, 2023. "Robust multi-objective optimization of safety barriers performance parameters for NaTech scenarios risk assessment and management," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:222:y:2008:i:2:p:115-126. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.