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A mixed integer genetic algorithm used in biological and chemical defense applications

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Abstract

There are many problems in security and defense that require a robust optimization technique, including those that involve the release of a chemical or biological contaminant. Our problem, in particular, is computing the parameters to be used in modeling atmospheric transport and dispersion given field sensor measurements of contaminant concentration. This paper discusses using a genetic algorithm for addressing this problem. An example is given how a mixed integer genetic algorithm can be used in conjunction with field sensor data to invert a forward model to obtain the meteorological data and source information necessary for prediction of the subsequent concentration field. A new mixed integer genetic algorithm is described that is a state-of-the-art tool capable of optimizing a wide range of objective functions. Such an algorithm is used here for optimizing atmospheric stability, wind speed, wind direction, rainout, and source location. We demonstrate that the algorithm is successful at reconstructing these meteorological and source parameters despite moderate correlations between their effects on the sensor data.

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Notes

  1. The algorithm was tested for sensitivity to changes in these parameters and found to be insensitive. Therefore, although this single case is shown here, we expect that the same results are attainable for other values of the variables.

  2. One way to address the coupling of Q and u is to employ a Gaussian puff model that provides a time-varying concentration field. In that case the additional information allows computing both parameters (Long et al. 2009). This approach, however, is not appropriate for the continuous release considered here.

  3. More generations are required to assure convergence when including these additional variables.

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Acknowledgments

The authors would like to thank Kerrie Long for making Fig. 2. Many helpful discussions with Christopher Allen, Kerrie Long, Anke Beyer-Lout, Andrew Annunzio, Yuki Kuroki, Lili Lei, and Luna Rodriguez helped inspire this work. The third author also expresses his eternal gratitude to Francis de Sales and John Bosco for support in manuscript preparation.

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Authors and Affiliations

  1. Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA, 16804, USA

    Sue Ellen Haupt & Randy L. Haupt

  2. Meteorology Department, The Pennsylvania State University, Walker Building, University Park, PA, 16804, USA

    Sue Ellen Haupt & George S. Young

Authors
  1. Sue Ellen Haupt
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  2. Randy L. Haupt
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  3. George S. Young
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Correspondence to Sue Ellen Haupt.

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Haupt, S.E., Haupt, R.L. & Young, G.S. A mixed integer genetic algorithm used in biological and chemical defense applications. Soft Comput 15, 51–59 (2011). https://doi.org/10.1007/s00500-009-0516-z

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  • DOI: https://doi.org/10.1007/s00500-009-0516-z

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