Robustness of optimisation algorithms for transonic aerodynamic design

In design optimisation problems, it is essential to ensure the convergence to the optimal design space with the lowest variability possible. In this respect, the optimisation algorithm plays a key role as it drives the exploration of the design space. This paper presents a statistical assessment of two genetic algorithms (NSGA-II and IBEA) and a particle swarm optimiser (OMOPSO) for the transonic aerodynamic design of compact nacelles for future aero-engines. OMOPSO is the most suitable optimisation algorithm due to the lowest variability to find an optimised design with a reasonable convergence rate of the optimisation.