Improving the Genetic Algorithm Performance in Aerial Spray Deposition Management

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Improving the Genetic Algorithm Performance in
Aerial Spray Deposition Management

• University of Georgia
• L. Wu, W.D. Potter, K. Rasheed
• USDA Forest Service
• J. Ghent, D. Twardus, H. Thistle
• Continuum Dynamics
• M. Teske

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Presentation Overview

• SAGA
• From SAGA to SAGA2
• From SAGA2 to SAGA2NN
• SAGADO
• Results
• Conclusion and future work

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SAGAaerial spray deposition management problem

• AGDISP (Aerial Spray Simulation Model) predicts
  the deposition of spray material released from an
  aircraft.
• The prediction is based on a set of spray
  parameter values as well as constant data. The
  total combination of possible spray parameters
  generates a huge search space (NP hard).
• SAGA (Spray Advisor using Genetic Algorithm) was
  developed to heuristically search for an optimal
  or near-optimal set of input parameters needed to
  achieve a certain aerial spray deposition.

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SAGAhow does SAGA work

• SAGA sends a set of spray parameters to AGDISP.
• AGDISP returns three spray output values VMD
  (the deposition composed of Volume Median
  Diameter), drift fraction, and COV (the
  Coefficient of Variance).
• Based on the fitness function values mapped from
  the spray output values, the GA attempts to
  evolve an improved set of parameters.

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SAGAfitness function

• The goal is to minimize the drift fraction,
  minimize the COV, and minimize the difference
  between the output VMD and the desired VMD.
• This is actually a multi-objective optimization
  problem, where a weighted-sum approach is
  applied.
• Fitness 100 ? 50 ? (1.0