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Progress in design optimization using evolutionary algorithms for aerodynamic problems
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DOI：10.1016/j.paerosci.2009.08.003.png)
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Evolutionary algorithms (EAs) are useful tools in design optimization. Due to their simplicity, ease of use, and suitability for multi-objective design optimization problems, EAs have been applied to design optimization problems from various areas. In this paper we review the recent progress in design optimization using evolutionary algorithms to solve real-world aerodynamic problems. Examples are given in the design of turbo pump, compressor, and micro-air vehicles. The paper covers the following topics that are deemed important to solve a large optimization problem from a practical viewpoint: (1) hybridized approaches to speed up the convergence rate of EAs; (2) the use of surrogate model to reduce the computational cost stemmed from EAs; (3) reliability based design optimization using EAs; and (4) data mining of Pareto-optimal solutions. Published by Elsevier Ltd.
Keyword:
Multi-objective design optimization
Evolutionary algorithms
Surrogate model
Robust and reliability-based design
Data mining
NASA rotor 67 blade
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