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Improving the Genetic Algorithm Performance in Aerial Spray Deposition Management

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. Presentation Overview. SAGA From SAGA to SAGA2 From SAGA2 to SAGA2NN

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Improving the Genetic Algorithm Performance in Aerial Spray Deposition Management

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  1. 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

  2. Presentation Overview • SAGA • From SAGA to SAGA2 • From SAGA2 to SAGA2NN • SAGADO • Results • Conclusion and future work

  3. SAGA:aerial 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.

  4. SAGA:how 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.

  5. SAGA:fitness 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 – DriftFraction) + 25  (1 -COV) + 25  VMDTerm], where VMDTerm = 1.0 – abs(1.0 – VMD/VMDCenter)

  6. From SAGA to SAGA2:the improvement of SAGA2 • The weakness of SAGA • The development of SAGA2 ((Spray Advisor using Genetic Algorithm version 2) • The improvement of SAGA2 • SAGA2 replaces the original generational genetic algorithm with a steady-state genetic algorithm. • SAGA2 replaces the original roulette wheel selection with tournament selection. • SAGA2 combines several kinds of crossover and mutation operators, and applies them with respective possibilities.

  7. From SAGA to SAGA2:the interface of SAGA2 the interface to customize SAGA2 parameters the main interface of SAGA2 the interface to preset spray parameters

  8. From SAGA2 to SAGA2NN:the improvement of SAGA2NN • The development of SAGA2NN (Spray Advisor using Genetic Algorithm version 2 with Neural Network) • The improvement of SAGA2NN • SAGA2NN generates the initial population from a large pool of individuals. • SAGA2NN does various crossover and mutation operations for each crossover and mutation, and selects the one with the highest fitness as the candidate. • SAGA2NN uses a neural network to approximate the fitness during the above process.

  9. From SAGA2 to SAGA2NN:how does a neural network work • Data mining • Data training • Learning rule: backpropagation with momentum • Interface of ANN: • Data mapping

  10. SAGADO • GADO (Genetic Algorithm for Design Optimization) is a general-purpose approach to solving the parametric design problem. • GADO uses a steady-state GA. • The selection scheme is ranking selection. • The replacement strategy is a crowding technique. • Several crossover and mutation operators are used, in which the most important one is guided crossover. • The development of SAGADO (Spray Advisor using Genetic Algorithm for Design Optimization).

  11. Results:general result • We ran these methods on several practical spray parameter specifications provided by Forest Service managers. • The convergence criterion is avgfitness/maxfitness>0.999. The GA will stop when it meets the convergence criterion, otherwise it stops after 5000 evaluations. • The maximum fitness values SAGA2 and SAGADO achieved are better than SAGA in every parameter setting. SAGA2NN obtains much better maximum fitness value in the first few hundred evaluations. Its lead is offset later, but it takes far fewer evaluations to converge.

  12. Results:evolution process of some parameter settings no variable constraint variable constraint: variable constraint: Aircraft ID=106, Aircraft ID=5, Swath Width=2.25Swath Width=2.3

  13. Conclusion and future work • Exquisite choice of type of GA, selection, crossover and mutation operator can boost GA performance. • Applying a neural network to the genetic algorithm does not achieve a much better result. We think the reason is that the advantage of the neural network is counteracted by premature convergence of the GA. SAGA2NN converges very fast, which is useful in real aerial spray applications because it can get the near-optimal result by far fewer simulations. • Currently we are working to apply other heuristic search approaches, such as simulated annealing, in aerial spray deposition management, and plan to compare their performance with SAGA2, SAGADO and SAGA2NN.

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