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

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  • 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
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    References listed on IDEAS

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    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.
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