Preference-based multiobjective particle swarm optimization for airfoil design

Abstract

A significant challenge to the application of evolutionary multiobjective optimization (EMO) for transonic airfoil design is the often excessive number of computational fluid dynamic (CFD) simulations required to ensure convergence. In this study, a multiobjective particle swarm op-timization (MOPSO) framework is introduced, which incorporates designer preferences to provide further guidance in the search. A reference point is projected onto the Pareto landscape by the designer to guide the swarm towards solutions of interest. The framework is applied to a typical transonic airfoil design scenario for robust aerodynamic performance. Time-adaptive Kriging models are constructed based on a high-fidelity Reynolds-averaged NavierStokes (RANS) solver to assess the performance of the solutions. The successful integration of these design tools is facilitated through the reference point, which ensures that the swarm does not deviate from the preferred search trajectory. A comprehensive discussion on the proposed optimization framework is provided, highlighting its viability for the intended design application.