Aircraft conceptual design using metaheuristic-based reliability optimisation

Champasak P., Panagant N., Pholdee N., Vio G. A., Bureerat S., YILDIZ B. S., ...More

AEROSPACE SCIENCE AND TECHNOLOGY, vol.129, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 129
  • Publication Date: 2022
  • Doi Number: 10.1016/j.ast.2022.107803
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: Reliability optimisation, Aircraft conceptual design, Reliability index, Most probable point, Metaheuristics, TOPOLOGY OPTIMIZATION, UNCERTAINTIES, TRUSSES, ROBUST, SHAPE
  • Bursa Uludag University Affiliated: Yes


The reliability optimisation methodology is developed to solve a conceptual design problem of a fixed-wing Unmanned Aerial Vehicle (UAV). The reliability quantification is based on the most probable point (MPP) concept, leading to a double-loop optimisation problem. The design problem is formulated as an outer-loop multiobjective optimisation for the main design problem and an inner-loop optimisation for estimating a reliability index (beta) of a design solution. The goal of the outer-loop optimisation is to minimise the aircraft take-off weight, and simultaneously maximise beta. The aerodynamic and stability properties of an aircraft solution are calculated by a vortex lattice method (VLM), while various types of empirical weight methods are obtained. The design problem is set up to have uncertainties in calculating aircraft empty weight and aerodynamic coefficients and derivatives. For the inner loop optimisation, the MPP is used for approximating the reliability index and probability of failure (pf). Multiobjective Meta-heuristic with Iterative Parameter Distribution Estimation (MMIPDE) and Success-History based Adaptive Differential Evolution (SHADE) are used for solving the outer- and inner-loop optimisations respectively. Four parameters setting strategies for running metaheuristics are proposed for use with the proposed metaheuristic-based reliability optimisation. The comparative results reveal that the best dynamic parameter setting from this study can reduce runtime by 22.5% compared to the traditional metaheuristic run while maintaining competitive results. (C) 2022 Elsevier Masson SAS. All rights reserved.