This paper describes an original application of unconventional optimization techniques by Particle Swarm Algorithms. An Unmanned Aerial Vehicle performed by Hot Wire Cutting is designed for a typical civil mission defining geometry and aerodynamics with a Particle Swarm Algorithm. The tailless configuration of the vehicle requires an accurate design to gain the satisfaction of all the requirements and to obtain a low cost solution. Only an unconventional technique can be applied because of the high non linearity of the problem and the high number of parameters to be defined. A first preliminary series of tests have been carried out to define the best values for inertia and acceleration coefficients of the Particle Swarm algorithm; in the following the algorithm results have been compared with those obtained by other two techniques like Genetic Algorithms and Monte Carlo Simulations. The result of this study shows how the Rapid Prototyping techniques can be applied to the performing of small lots of UAV: the required optimal design is gained applying the Particle Swarm Algorithm. The conclusion of this work confirms the suitability of non conventional 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 Particle Swarm and Monte Carlo algorithms are simple to implement within a software code with respect to the Genetic Algorithms which are quite difficult to code.

Optimization by Particle Swarm Algorithms of an UAV performed by Hot Wire Cutting Techniques

CERUTI, ALESSANDRO;CALIGIANA, GIANNI;PERSIANI, FRANCO
2011

Abstract

This paper describes an original application of unconventional optimization techniques by Particle Swarm Algorithms. An Unmanned Aerial Vehicle performed by Hot Wire Cutting is designed for a typical civil mission defining geometry and aerodynamics with a Particle Swarm Algorithm. The tailless configuration of the vehicle requires an accurate design to gain the satisfaction of all the requirements and to obtain a low cost solution. Only an unconventional technique can be applied because of the high non linearity of the problem and the high number of parameters to be defined. A first preliminary series of tests have been carried out to define the best values for inertia and acceleration coefficients of the Particle Swarm algorithm; in the following the algorithm results have been compared with those obtained by other two techniques like Genetic Algorithms and Monte Carlo Simulations. The result of this study shows how the Rapid Prototyping techniques can be applied to the performing of small lots of UAV: the required optimal design is gained applying the Particle Swarm Algorithm. The conclusion of this work confirms the suitability of non conventional 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 Particle Swarm and Monte Carlo algorithms are simple to implement within a software code with respect to the Genetic Algorithms which are quite difficult to code.
IMPROVE 2011
646
653
A. Ceruti; G. Caligiana; F. Persiani
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/103140
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