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

Soft Computing (Impact Factor: 1.27). 01/2011; 15(1):51-59. DOI: 10.1007/s00500-009-0516-z
Source: DBLP


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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    • "The first approach uses a GA to back calculate source and meteorological data. This method has also been proven to work with identical twin data (Haupt 2005; Allen et al. 2007a,b; Haupt et. al. 2007, 2009, 2010; Long et. al. 2010; Rodriguez et al. 2010). We begin with a set of trial solutions that are then fed into an AT&D model."
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    ABSTRACT: Source term estimation algorithms compute unknown atmospheric transport and dispersion modeling variables from concentration observations made by sensors in the field. Insufficient spatial and temporal resolution in the meteorological data as well as inherent uncertainty in the wind field data make source term estimation and the prediction of subsequent transport and dispersion extremely difficult. This work addresses the question: how many sensors are necessary in order to successfully estimate the source term and meteorological variables required for atmospheric transport and dispersion modeling?The source term estimation system presented here uses a robust optimization technique – a genetic algorithm (GA) – to find the combination of source location, source height, source strength, surface wind direction, surface wind speed, and time of release that produces a concentration field that best matches the sensor observations. The approach is validated using the Gaussian puff as the dispersion model in identical twin numerical experiments. The limits of the system are tested by incorporating additive and multiplicative noise into the synthetic data. The minimum requirements for data quantity and quality are determined by an extensive grid sensitivity analysis. Finally, a metric is developed for quantifying the minimum number of sensors necessary to accurately estimate the source term and to obtain the relevant wind information.
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