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Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK
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Mach. Learn. Knowl. Extr. 2019, 1(2), 552-574; https://doi.org/10.3390/make1020033
Received: 5 March 2019 / Revised: 22 March 2019 / Accepted: 30 March 2019 / Published: 4 April 2019
This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem. View Full-Text
Keywords: machine learning; data visualization; Multi-Objective Particle Swarm Optimisation; Multi-Objective Tabu Search; nimrod/tool; parallel coordinates; Athena Vortex Lattice machine learning; data visualization; Multi-Objective Particle Swarm Optimisation; Multi-Objective Tabu Search; nimrod/tool; parallel coordinates; Athena Vortex Lattice
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Azabi, Y.; Savvaris, A.; Kipouros, T. Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV. Mach. Learn. Knowl. Extr. 2019, 1, 552-574.

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