PCA-Enhanced Metamodel-Assisted Evolutionary Algorithms for Aerodynamic Optimization
This paper deals with evolutionary algorithms (EAs) assisted by surrogate evaluation models or metamodels (metamodel-assisted EAs, MAEAs) which are further accelerated by exploiting the principal component analysis (PCA) of the elite members of the evolving population. In each generation of the MAEA, PCA is used to (a) better guide the application of evolution operators and (b) train metamodels, in the form of radial basis functions networks, on patterns of smaller dimension. Note that the present MAEA relies upon “local” metamodels which are trained on-line, separately for each and every population member. Compared to previous works by the same authors, this paper proposes a new way to apply the PCA technique. In particular, the front of non-dominated solutions is divided into sub-fronts and the PCA is applied “locally” to each sub-front. The proposed method is demonstrated in multi-objective, constrained, aerodynamic optimization problems.
KeywordsPrincipal Component Analysis Design Variable Pareto Front Radial Basis Function Network Wind Turbine Blade
The authors express their thanks to Professors S. Voutsinas and V. Riziotis, NTUA, for providing the necessary data and evaluation software for the wind turbine aeroelastic optimization case and their constructive comments and suggestions. This study has been co-financed by the European Union (European Social Fund-ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.
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