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Genetic multistart algorithm for the design of fault-tolerant systems

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    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.
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