A self-adaptive genetic algorithm applied to multi-objective optimization of an airfoil

Genetic algorithms (GAs) have been used to tackle non-linear multi-objective optimization (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 optimizing GA (MOGA) that uses self-adaptive mutation and crossover, and which is applied to optimization of an airfoil, for minimization of drag and maximization of lift coefficients. The MOGA 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.

Plenary talk - Applied HTS research and practical applications in Europe

The development of long-length, high current density Bi2Sr 2CaCu2Ox wires and (RE)Ba2Cu 3Oy coated conductors has now advanced such that superconducting magnets for energy applications and high field applications are progressing rapidly. Europe, along with China, Korea the US and Japan is an important player in the development and exploitation of High Temperature Superconductors in practical applications. © 2013 IEEE.