Multi-objective Optimisation by Self-adaptive Evolutionary Algorithm

Evolutionary algorithms (EAs) have been used to tackle non-linear multiobjective optimisation (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimising EA (MOOEA) that uses self-adaptive mutation and crossover, and which is applied to optimisation of an airfoil, for minimisation of drag and maximisation of lift coefficients. The MOOEA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.