Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle

• Published in: Aerospace science and technology Vol. 100; p. 105783
• Main Authors: Champasak, Pakin, Panagant, Natee, Pholdee, Nantiwat, Bureerat, Sujin, Yildiz, Ali Riza
• Format: Journal Article
• Language: English
• Published: Elsevier Masson SAS 01.05.2020
• Subjects: Aircraft conceptual design; Aircraft performance; Dynamic stability; Many-objective optimisation; Aircraft performance; Aircraft conceptual design; Many-objective optimisation; Dynamic stability
• ISSN: 1270-9638; 1626-3219
• DOI: 10.1016/j.ast.2020.105783

Many-objective optimisation is a design problem, having more than 3 objective functions, which is found to be difficult to solve. Implementation of such optimisation on aircraft conceptual design will greatly benefit a design team, as a great number of trade-off design solutions are provided for further decision making. In this paper, a many-objective optimisation problem for an unmanned aerial vehicle (UAV) is posed with 6 objective functions: take-off gross weight, drag coefficient, take off distance, power required, lift coefficient and endurance subject to aircraft performance and stability constraints. Aerodynamic analysis is carried out using a vortex lattice method, while aircraft component weights are estimated empirically. A new self-adaptive meta-heuristic based on decomposition is specifically developed for this design problem. The new algorithm along with nine established and recently developed multi-objective and many-objective meta-heuristics are employed to solve the problem, while comparative performance is made based upon a hypervolume indicator. The results reveal that the proposed optimiser is the best performer for this design task.