Progress in design optimization using evolutionary algorithms for aerodynamic problems

• Published in: Progress in aerospace sciences Vol. 46; no. 5; pp. 199 - 223
• Main Authors: Lian, Yongsheng, Oyama, Akira, Liou, Meng-Sing
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2010
• Subjects: Aerodynamics; Aerospace sciences; Compressors; Computational efficiency; Convergence; Data mining; Design optimization; Evolutionary algorithms; Multi-objective design optimization; NASA rotor 67 blade; Optimization; Robust and reliability-based design; Surrogate model; Turbopumps; NASA rotor 67 blade; Evolutionary algorithms; Surrogate model; Data mining; Multi-objective design optimization; Robust and reliability-based design
• ISSN: 0376-0421; 1873-1724
• DOI: 10.1016/j.paerosci.2009.08.003

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.