Comparative evaluation of different optimization methodologies for the design of UAVs having shape obtained by hot wire cutting techniques

This paper describes an original application of heuristic optimization techniques to a complex multidisciplinary task. An unmanned aerial vehicle with a shape obtained by hot wire cutting techniques is designed for a typical civil mission, defining its geometry and aerodynamics with a particle swarm algorithm, a genetic algorithm and a Monte Carlo simulation. The tailless configuration of the vehicle requires an accurate design to satisfy all the requirements and obtain a low cost solution; only heuristic or semi-heuristic techniques can be applied because of the high non linearity of the problem and the large number of parameters to be defined. The three optimization methodologies have been applied to the problem, comparing their effectiveness on the basis of the computational weight. This study shows how the Rapid Prototyping techniques can be applied to the manufacturing of small lots of UAVs: the required optimal design is gained applying heuristic optimization techniques. The conclusions which can be drawn from this work confirm the suitability of optimization methods to non linear problems: genetic algorithms and particle swarm optimization provide similar results in term of fitness maximization, while Monte Carlo algorithm presents a lower efficiency. The easy implementation of the particle swarm optimization algorithm, compared to the more complex genetic algorithm, suggests how to use the former in optimization problems related to product design.

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