IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v103y2012icp61-71.html
   My bibliography  Save this article

Multi-objective optimization of a series–parallel system using GPSIA

Author

Listed:
  • Okafor, Ekene Gabriel
  • Sun, You-Chao

Abstract

The optimal solution of a multi-objective optimization problem (MOP) corresponds to a Pareto set that is characterized by a tradeoff between objectives. Genetic Pareto Set Identification Algorithm (GPSIA) proposed for reliability-redundant MOPs is a hybrid technique which combines genetic and heuristic principles to generate non-dominated solutions. Series–parallel system with active redundancy is studied in this paper. Reliability and cost were the research objective functions subject to cost and weight constraints. The results reveal an evenly distributed non-dominated front. The distances between successive Pareto points were used to evaluate the general performance of the method. Plots were also used to show the computational results for the type of system studied and the robustness of the technique is discussed in comparison with NSGA-II and SPEA-2.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:103:y:2012:i:c:p:61-71
    DOI: 10.1016/j.ress.2012.03.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832012000488
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2012.03.014?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tian, Zhigang & Zuo, Ming J., 2006. "Redundancy allocation for multi-state systems using physical programming and genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1049-1056.
    2. Gen, Mitsuo & Yun, YoungSu, 2006. "Soft computing approach for reliability optimization: State-of-the-art survey," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1008-1026.
    3. 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.
    4. Tavakkoli-Moghaddam, R. & Safari, J. & Sassani, F., 2008. "Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 550-556.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kong, Xiangyong & Gao, Liqun & Ouyang, Haibin & Li, Steven, 2015. "Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 147-158.

    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. Coelho, Leandro dos Santos, 2009. "An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 830-837.
    2. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2012. "Multi-objective optimization of design and testing of safety instrumented systems with MooN voting architectures using a genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 45-60.
    3. Innal, Fares & Dutuit, Yves & Chebila, Mourad, 2015. "Safety and operational integrity evaluation and design optimization of safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 32-50.
    4. Li, Chun-yang & Chen, Xun & Yi, Xiao-shan & Tao, Jun-yong, 2010. "Heterogeneous redundancy optimization for multi-state series–parallel systems subject to common cause failures," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 202-207.
    5. Li, Zhaojun & Liao, Haitao & Coit, David W., 2009. "A two-stage approach for multi-objective decision making with applications to system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1585-1592.
    6. Prashanthi Boddu & Liudong Xing, 2013. "Reliability evaluation and optimization of series–parallel systems with k-out-of-n: G subsystems and mixed redundancy types," Journal of Risk and Reliability, , vol. 227(2), pages 187-198, April.
    7. 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.
    8. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2013. "Cold-standby sequencing optimization considering mission cost," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 28-34.
    9. Attar, Ahmad & Raissi, Sadigh & Khalili-Damghani, Kaveh, 2017. "A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 177-191.
    10. L Podofillini & E Zio, 2008. "Events group risk importance by genetic algorithms," Journal of Risk and Reliability, , vol. 222(3), pages 337-346, September.
    11. 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).
    12. 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.
    13. Coit, David W. & Zio, Enrico, 2019. "The evolution of system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    14. 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.
    15. 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.
    16. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2009. "Design optimization of a safety-instrumented system based on RAMS+C addressing IEC 61508 requirements and diverse redundancy," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 162-179.
    17. Sadjadi, Seyed Jafar & Soltani, R., 2009. "An efficient heuristic versus a robust hybrid meta-heuristic for general framework of serial–parallel redundancy problem," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1703-1710.
    18. Mohammad N. Juybari & Pardis Pourkarim Guilani & Mostafa Abouei Ardakan, 2022. "Bi-objective sequence optimization in reliability problems with a matrix-analytic approach," Annals of Operations Research, Springer, vol. 312(1), pages 275-304, May.
    19. Dolatshahi-Zand, Ali & Khalili-Damghani, Kaveh, 2015. "Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 11-21.
    20. Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.

    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:eee:reensy:v:103:y:2012:i:c:p:61-71. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.