Oliver, JM and Kipouros, T and Savill, AM (2013) A self-adaptive genetic algorithm applied to multi-objective optimization of an airfoil. Advances in Intelligent Systems and Computing, 227. pp. 261-276. ISSN 2194-5357Full text not available from this repository.
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.
|Uncontrolled Keywords:||Airfoil Algorithm GA Genetic MOGA MOO MOOP Multi-Objective Optimization Parameters Self-Adaptive|
|Divisions:||Div C > Engineering Design|
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 12:04|
|Last Modified:||26 Jan 2015 02:59|