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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
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    Cited by:

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