IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v222y2008i1p17-29.html
   My bibliography  Save this article

Genetic multistart algorithm for the design of fault-tolerant systems

Author

Listed:
  • K Echtle
  • I Eusgeld
  • D Hirsch

Abstract

This paper presents a new approach to the multiobjective design of fault-tolerant systems. The design objectives are fault tolerance and cost . Reducing the cost is of particular importance for fault-tolerant systems because the overhead caused by redundant components is considerable. The new design method consists of a special genetic algorithm that is tailored to the particular issues of fault-tolerant systems. The interface of the present tool ePADuGA (elitist and Pareto-based Approach to Design fault-tolerant systems using a Genetic Algorithm) allows for adaptation to various fields of application. The degree of fault tolerance is measured by the number of tolerated faults rather than traditional reliability metrics, because reliability numbers are mostly unknown during early design phases. The special features of the genetic algorithm comprise a graph-oriented representation of systems (which are the individuals during the evolutionary process), a simple yet expressive fault model, a very efficient procedure for fault-tolerance evaluation, and a Pareto-oriented fitness function. In a genetic algorithm generating thousands of individuals, a very fast evaluation of each individual is mandatory. For this purpose, state-space-oriented evaluation methods have been cut down to an extremely simple function which is still sufficient to assess the fault tolerance of individuals. An innovative aspect is also a multistart technique to find a Pareto solution set, which is independent of any parameters. In this paper, experimental results are presented showing the feasibility of the approach as well as the usefulness of the final fault-tolerant architectures, particularly in the field of mechatronic systems.

Suggested Citation

  • K Echtle & I Eusgeld & D Hirsch, 2008. "Genetic multistart algorithm for the design of fault-tolerant systems," Journal of Risk and Reliability, , vol. 222(1), pages 17-29, March.
    • Handle: RePEc:sae:risrel:v:222:y:2008:i:1:p:17-29
    DOI: 10.1243/1748006XJRR70
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1243/1748006XJRR70
    Download Restriction: no
    File URL: https://libkey.io/10.1243/1748006XJRR70?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. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    2. Kulturel-Konak, Sadan & Smith, Alice E. & Norman, Bryan A., 2006. "Multi-objective tabu search using a multinomial probability mass function," European Journal of Operational Research, Elsevier, vol. 169(3), pages 918-931, March.
    3. Jones, D. F. & Mirrazavi, S. K. & Tamiz, M., 2002. "Multi-objective meta-heuristics: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 137(1), pages 1-9, February.
    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. Selçuklu, Saltuk Buğra & Coit, David W. & Felder, Frank A., 2020. "Pareto uncertainty index for evaluating and comparing solutions for stochastic multiple objective problems," European Journal of Operational Research, Elsevier, vol. 284(2), pages 644-659.
    2. Lee, Sangkeum & Cho, Hong-Yeon & Har, Dongsoo, 2018. "Operation optimization with jointly controlled modules powered by hybrid energy source: A case study of desalination," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3070-3080.
    3. Abouei Ardakan, Mostafa & Rezvan, Mohammad Taghi, 2018. "Multi-objective optimization of reliability–redundancy allocation problem with cold-standby strategy using NSGA-II," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 225-238.
    4. Tarik Chargui & Abdelghani Bekrar & Mohamed Reghioui & Damien Trentesaux, 2019. "Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption," Sustainability, MDPI, vol. 11(11), pages 1-23, June.
    5. Gupta, Pankaj & Mittal, Garima & Mehlawat, Mukesh Kumar, 2013. "Expected value multiobjective portfolio rebalancing model with fuzzy parameters," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 190-203.
    6. Weifan Zhong & Lijing Du, 2023. "Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    7. Schweiger, Katharina & Sahamie, Ramin, 2013. "A hybrid Tabu Search approach for the design of a paper recycling network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 50(C), pages 98-119.
    8. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
    9. J. Octavio Gutierrez-Garcia & Kwang Mong Sim, 2012. "GA-based cloud resource estimation for agent-based execution of bag-of-tasks applications," Information Systems Frontiers, Springer, vol. 14(4), pages 925-951, September.
    10. Cai, Yuhao & Qian, Xin & Su, Ruihang & Jia, Xiongjie & Ying, Jinhui & Zhao, Tianshou & Jiang, Haoran, 2024. "Thermo-electrochemical modeling of thermally regenerative flow batteries," Applied Energy, Elsevier, vol. 355(C).
    11. Ahmadi, Mohammad H. & Amin Nabakhteh, Mohammad & Ahmadi, Mohammad-Ali & Pourfayaz, Fathollah & Bidi, Mokhtar, 2017. "Investigation and optimization of performance of nano-scale Stirling refrigerator using working fluid as Maxwell–Boltzmann gases," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 337-350.
    12. Hausken, Kjell & Levitin, Gregory, 2009. "Minmax defense strategy for complex multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 577-587.
    13. Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
    14. Surafel Luleseged Tilahun & Mohamed A. Tawhid, 2019. "Swarm hyperheuristic framework," Journal of Heuristics, Springer, vol. 25(4), pages 809-836, October.
    15. T. Gómez & M. Hernández & J. Molina & M. León & E. Aldana & R. Caballero, 2011. "A multiobjective model for forest planning with adjacency constraints," Annals of Operations Research, Springer, vol. 190(1), pages 75-92, October.
    16. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
    17. Alarcon-Rodriguez, Arturo & Ault, Graham & Galloway, Stuart, 2010. "Multi-objective planning of distributed energy resources: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(5), pages 1353-1366, June.
    18. Prina, Matteo Giacomo & Lionetti, Matteo & Manzolini, Giampaolo & Sparber, Wolfram & Moser, David, 2019. "Transition pathways optimization methodology through EnergyPLAN software for long-term energy planning," Applied Energy, Elsevier, vol. 235(C), pages 356-368.
    19. Janssens, Jochen & Van den Bergh, Joos & Sörensen, Kenneth & Cattrysse, Dirk, 2015. "Multi-objective microzone-based vehicle routing for courier companies: From tactical to operational planning," European Journal of Operational Research, Elsevier, vol. 242(1), pages 222-231.
    20. H. Liao & Q. Wu, 2013. "Multi-objective optimization by learning automata," Journal of Global Optimization, Springer, vol. 55(2), pages 459-487, February.

    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:1:p:17-29. 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.