Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Future Generation Computer Systems
Development of an efficient aerodynamic shape optimization framework
Mathematics and Computers in Simulation
A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization
Genetic Programming and Evolvable Machines
Short term wind speed prediction based on evolutionary support vector regression algorithms
Expert Systems with Applications: An International Journal
Estimating the shift size in the process mean with support vector regression and neural networks
Expert Systems with Applications: An International Journal
Nonlinear estimation of transient flow field low dimensional states using artificial neural nets
Expert Systems with Applications: An International Journal
Grey relational grade in local support vector regression for financial time series prediction
Expert Systems with Applications: An International Journal
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
Advances in Engineering Software
Hi-index | 12.05 |
The shortening of the design cycle and the increase of the performance are nowadays the main challenges in aerodynamic design. The use of evolutionary algorithms (EAs) seems to be appropriate in a preliminary phase, due to their ability to broadly explore the design space and obtain global optima. Evolutionary algorithms have been hybridized with metamodels (or surrogate models) in several works published in the last years, in order to substitute expensive computational fluid dynamics (CFD) simulations. In this paper, an advanced approach for the aerodynamic optimization of aeronautical wing profiles is proposed, consisting of an evolutionary programming algorithm hybridized with a support vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size and feasibility of the complete approach are discussed and the potential of global optimization methods (enhanced by metamodels) to achieve innovative shapes that would not be achieved with traditional methods is assessed.