Nature Inspired Heuristics in Aerial Spray Deposition Management

AGDISP (Aerial Spray Simulation Model) is used to predict the deposition of spray material released from an aircraft. Determining the optimal input values to AGDISP in order to produce a desired spray material deposition is extremely difficult (NP hard). SAGA, an intelligent optimization method based on the simple genetic algorithm, was developed to solve this problem. In this paper, we apply several nature inspired heuristics to this problem. The first method still uses the genetic algorithm, but changes its type, selection method, crossover and mutation operator. The second method applies a neural network to improve the initial population, crossover and mutation. The third method uses GADO, a general-purpose approach to solving the parametric design problem. The fourth method applies simulated annealing to this problem. Finally, we compare their performance with SAGA and discuss their applications to the aerial spray deposition problem.