Evolutionary Algorithms for Multi-Objective Design Optimization

This article presents the general principles of Evolutionary Algorithms (EAs), along with a series of applications in the field of aeronautics. Classical EAs are good enough for problems based on simple mathematical models (i.e. linear models) . However, as the applications evolve in complexity, we had to develop new algorithms with better capabilities: among these, we will mostly focus on algorithms combining EAs and Game Theory (hence enabling the algorithm to deal with multi-criteria problems) as well as EAs with a hierarchical structure (which speeds up the convergence by using models of increasing complexity) . These concepts are then illustrated via experiments on several applications: minimization of the Radar Cross Section (RCS) around a multi-element airfoil in CEM, reconstruction of a 2D nozzle using multiple CFD models, and a coupled minimization (CEM + CFD) of the drag and RCS for an airfoil. These examples open the way for future applications of EAs in multi-disciplinary design optimization.