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

Champasak P., Panagant N., Pholdee N., Bureerat S., YILDIZ A. R.

AEROSPACE SCIENCE AND TECHNOLOGY, cilt.100, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 100
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.ast.2020.105783
  • Dergi Adı: AEROSPACE SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Aircraft conceptual design, Many-objective optimisation, Aircraft performance, Dynamic stability, MULTIOBJECTIVE EVOLUTIONARY ALGORITHM, AERODYNAMIC SHAPE OPTIMIZATION, UAV, SYSTEM
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

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. (C) 2020 Elsevier Masson SAS. All rights reserved.