Grid-based many-objective optimiser for aircraft conceptual design with multiple aircraft configurations

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

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.126, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 126
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.engappai.2023.106951
  • Dergi Adı: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: Multi -configurations, Aircraft conceptual design, Many -objective optimisation, Iterative parameter distribution estimation, Metaheuristics, Aircraft performance, GENETIC ALGORITHMS, MULTIOBJECTIVE OPTIMIZATION, DIFFERENTIAL EVOLUTION, AERODYNAMIC DESIGN
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

This paper presents an aircraft conceptual design technique with more than three objective functions, called many-objective optimisation. The selection of aircraft configuration is usually achieved using a system engineering approach. This selection approach has the design variables assigned to remove the configuration decision-making process. The design problem is demonstrated for the conceptual design of a fixed-wing unmanned aerial vehicle. Eight objective functions, including power required, take-off weight, take-off distance, landing distance, endurance, range, lift coefficient at cruise and drag coefficient at cruise, are posed, while the constraints are aircraft stability, performance required and take-off distance. Design variables simultaneously determine an aircraft configuration, shape and sizing parameters. Hence a new, many-objective metaheuristic is developed to increase the design performance. A grid-based many-objective metaheuristic with iterative parameter distribution estimation (MM-IPDE-Gr) is developed. It is an enhanced variant of the MM-IPDE with improved reproduction schemes, adaptive parameters and a grid-based clustering technique. Several additional reproduction schemes in mutation and crossover processes with two additional adaptive parameters are integrated to increase population diversity and improve the exploration ability of the algorithm. In addition, the gridbased method is integrated as a clustering technique to improve the Pareto clustering process in many-objective optimisation. The proposed method, with established newly invented metaheuristics, is used to solve the new design problem and its performance compared with existing design methods. It is shown that the proposed manyobjective metaheuristic gives the best results.