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.