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The application of genetic algorithms to optimise the performance of a mine ventilation network: the influence of coding method and population size

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Abstract

This paper presents an application of genetic algorithms (GAs) to the solution of a real-world optimisation problem. The proposed GA method investigates the optimisation of a mine ventilation system to minimise the operational fan power costs by the determination of the most effective combination of the fan operational duties and locations. The paper examines the influence that both the encoding method and the population size have on the performance of the GA. The relative performance of the GA produced by the use of two different encoding methods (a binary and a hybrid code) and various solution population sizes is assessed by performing a two way ANOVA analysis. It is concluded that the genetic algorithm approach offers both an effective and efficient optimisation method in the selection and evaluation of the cost-effective solutions in the planning and operation of mine ventilation systems.

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Authors and Affiliations

  1. School of Chemical, Environmental and Mining Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom

    I. S. Lowndes

  2. School of Computing, Information Systems & Mathematics, South Bank University, 103 Borough Road, London, SE1 0AA, United Kingdom

    T. Fogarty

  3. School of Computing, Information Systems & Mathematics, South Bank University, 103 Borough Road, London, SE1 0AA, United Kingdom

    Z. Y. Yang

Authors
  1. I. S. Lowndes
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  2. T. Fogarty
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  3. Z. Y. Yang
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Correspondence to I. S. Lowndes.

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Lowndes, I., Fogarty, T. & Yang, Z. The application of genetic algorithms to optimise the performance of a mine ventilation network: the influence of coding method and population size. Soft Comput 9, 493–506 (2005). https://doi.org/10.1007/s00500-004-0364-9

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  • DOI: https://doi.org/10.1007/s00500-004-0364-9

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