IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v220y2006i2p77-91.html
   My bibliography  Save this article

Optimization of system reliability using chaos-embedded self-organizing hierarchical particle swarm optimization

Author

Listed:
  • M Bachlaus
  • N Shukla
  • M. K. Tiwari
  • R Shankar

Abstract

This paper addresses a reliability optimization problem, where the motive is to select the best components for series and series-parallel systems such that system reliability becomes maximized while simultaneously minimizing the cost, weight, and volume. Previous formulation of the problem has implicit restrictions, i.e. it either maximizes system reliability or minimizes the cost. Thus, in order to give a realistic view to the model, a comprehensive objective function has been formulated by combining the normalized values of reliability, cost, weight, and volume. In this paper, a chaos-embedded hierarchical particle swarm optimization (CE-HPSO) algorithm has been proposed to solve the problems arising in the optimization of system reliability using redundancy. The salient features of the proposed algorithm are the use of chaotic sequences and time-varying acceleration coefficients which are responsible for diversifying the search space. Moreover, to restrict the premature convergence, a hierarchical particle swarm optimizer has been used in the proposed algorithm. The performance of the CE-HPSO algorithm has been tested on three benchmark problems and the comparisons are made with genetic algorithm results. In order to check the scalability of the proposed solution methodology, small and large problems are also considered. The results demonstrate the benefits of the proposed algorithm for solving this type of problem.

Suggested Citation

  • M Bachlaus & N Shukla & M. K. Tiwari & R Shankar, 2006. "Optimization of system reliability using chaos-embedded self-organizing hierarchical particle swarm optimization," Journal of Risk and Reliability, , vol. 220(2), pages 77-91, December.
    • Handle: RePEc:sae:risrel:v:220:y:2006:i:2:p:77-91
    DOI: 10.1243/1748006XJRR23
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1243/1748006XJRR23
    Download Restriction: no
    File URL: https://libkey.io/10.1243/1748006XJRR23?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. F. A. Tillman & J. M. Liittschwager, 1967. "Integer Programming Formulation of Constrained Reliability Problems," Management Science, INFORMS, vol. 13(11), pages 887-899, July.
    2. David W. Coit & Alice E. Smith & David M. Tate, 1996. "Adaptive Penalty Methods for Genetic Optimization of Constrained Combinatorial Problems," INFORMS Journal on Computing, INFORMS, vol. 8(2), pages 173-182, May.
    3. 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.
    4. Paul A. Jensen, 1970. "Optimization of Series-Parallel-Series Networks," Operations Research, INFORMS, vol. 18(3), pages 471-482, June.
    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. Rashika Gupta & Manju Agarwal, 2006. "Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system," Journal of Combinatorial Optimization, Springer, vol. 12(3), pages 257-277, November.
    2. Dunker, Thomas & Radons, Gunter & Westkamper, Engelbert, 2005. "Combining evolutionary computation and dynamic programming for solving a dynamic facility layout problem," European Journal of Operational Research, Elsevier, vol. 165(1), pages 55-69, August.
    3. Yuji Nakagawa & Ross J. W. James & César Rego & Chanaka Edirisinghe, 2014. "Entropy-Based Optimization of Nonlinear Separable Discrete Decision Models," Management Science, INFORMS, vol. 60(3), pages 695-707, March.
    4. Bosse, Sascha & Splieth, Matthias & Turowski, Klaus, 2016. "Multi-objective optimization of IT service availability and costs," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 142-155.
    5. 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.
    6. Sadan Kulturel-Konak & Bryan A. Norman & David W. Coit & Alice E. Smith, 2004. "Exploiting Tabu Search Memory in Constrained Problems," INFORMS Journal on Computing, INFORMS, vol. 16(3), pages 241-254, August.
    7. Subba Rao V. Majety & Milind Dawande & Jayant Rajgopal, 1999. "Optimal Reliability Allocation with Discrete Cost-Reliability Data for Components," Operations Research, INFORMS, vol. 47(6), pages 899-906, December.
    8. Bin Xu & Ping-An Zhong & Xinyu Wan & Weiguo Zhang & Xuan Chen, 2012. "Dynamic Feasible Region Genetic Algorithm for Optimal Operation of a Multi-Reservoir System," Energies, MDPI, vol. 5(8), pages 1-17, August.
    9. José-Fernando Camacho-Vallejo & Lilian López-Vera & Alice E. Smith & José-Luis González-Velarde, 2022. "A tabu search algorithm to solve a green logistics bi-objective bi-level problem," Annals of Operations Research, Springer, vol. 316(2), pages 927-953, September.
    10. 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.
    11. F. Zeynep Sargut & Caner Altuntaş & Dilek Cetin Tulazoğlu, 2017. "Multi-objective integrated acyclic crew rostering and vehicle assignment problem in public bus transportation," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 1071-1096, October.
    12. Michael Brusco & Patrick Doreian & Douglas Steinley & Cinthia Satornino, 2013. "Multiobjective Blockmodeling for Social Network Analysis," Psychometrika, Springer;The Psychometric Society, vol. 78(3), pages 498-525, July.
    13. Christopher Garcia, 2018. "Optimal multiunit transfer over adversarial paths with increasing intercept probabilities," IISE Transactions, Taylor & Francis Journals, vol. 50(11), pages 989-996, November.
    14. Umesh Balande & Deepti Shrimankar, 2020. "An oracle penalty and modified augmented Lagrangian methods with firefly algorithm for constrained optimization problems," Operational Research, Springer, vol. 20(2), pages 985-1010, June.
    15. Yalaoui, Alice & Chu, Chengbin & Châtelet, Eric, 2005. "Reliability allocation problem in a series–parallel system," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 55-61.
    16. Wattanapongskorn, Naruemon & Coit, David W., 2007. "Fault-tolerant embedded system design and optimization considering reliability estimation uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 395-407.
    17. J. Gago-Vargas & I. Hartillo & J. Puerto & J. Ucha, 2015. "An improved test set approach to nonlinear integer problems with applications to engineering design," Computational Optimization and Applications, Springer, vol. 62(2), pages 565-588, November.
    18. 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.
    19. Young Woong Park, 2020. "MILP Models for Complex System Reliability Redundancy Allocation with Mixed Components," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 600-619, July.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:220:y:2006:i:2:p:77-91. 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.