Open Access
Volume 37, Number 2, April 2019
Page(s) 232 - 241
Published online 05 August 2019
  1. Qu X, Zhang R, LIU B, et al. An Improved TLBO Based Memetic Algorithm for Aerodynamic Shape Optimization[J]. Engineering Applications of Artificial Intelligence, 2017, 57: 1–15 [Article] [CrossRef] [Google Scholar]
  2. Leifsson L, Koziel S, Tesfahunegn Y A. Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates[J]. AIAA Journal, 2016, 54: 531–541 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  3. Ebrahimi M, Jahangirian AAccelerating Global Optimization of Aerodynamic Shapes Using a New Surrogate-Assisted Parallel Genetic Algorithm[J]. Engineering Optimization, 2017, 49(12): 1–16 [Article] [CrossRef] [Google Scholar]
  4. Cao L, Zhang D. Aerodynamic Configuration Optimization for Hypersonic Gliding Vehicle Based on Improved Hybrid Multi-Objective PSO Algorithm[C]//IEEE International Conference on Signal Processing, Communications and Computing, 2015: 1–5 [Google Scholar]
  5. Song L, Luo C, Li J, et al. Aerodynamic Optimization of Axial Turbomachinery Blades Using Parallel Adaptive Range Differential Evolution and Reynolds-Averaged Navier-Stokes Solutions[J]. International Journal for Numerical Methods in Biomedical Engineering, 2011, 27(2): 283–303 [Article] [CrossRef] [Google Scholar]
  6. Weishuang L U, Tian Y, Liu PAerodynamic Optimization and Mechanism Design of Flexible Variable Camber Trailing-Edge Flap[J]. Chinese Journal of Aeronautics, 2017, 30(3): 988–1003 [Article] [CrossRef] [Google Scholar]
  7. Koo D, Zingg D W. Investigation into Aerodynamic Shape Optimization of Planar and Nonplanar Wings[J]. AIAA Journal, 2017, 56(1): 1–14 [Google Scholar]
  8. Li Ding, Xia LuApplication of a Hybrid Particle Swarm Optimization to Airfoil Design Aeronautical Computing Technique, 2010, 40(6): 66–71 (in Chinese) [Article] [Google Scholar]
  9. Chen Jin, Guo Xiaofeng, Sun Zhenye, et al. Optimization of Wind Turbine Thick Airfoils Using Improved Multi-Objective Particle Swarm Algorithm Journal of Northeastern University, 2016, 37(2): 232–236 (in Chinese) [Article] [Google Scholar]
  10. Li Xin, Qu Zhuanli, Li Geng, et al. A Numerical Optimization for High Efficiency and Low Noise Airfoils Journal of Vibration and Shock, 2017, 36(4): 66–72 (in Chinese) [Article] [Google Scholar]
  11. Weron A, Weron R. Computer Simulation of Levy-α Stable Variables and Processes. Poland, Springer, Berlin Heidelberg 1995379–392 [Google Scholar]
  12. Kogon S M, Manolakis D G. Signal Modeling with Self-Similar α Stable Processes:The Fractional Levy Stable Motion Model[J]. IEEE Trans on Signal Processing, 1996, 44(4): 1006–1010 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  13. Wei J L, Jambek A B, Neoh S C. Kursawe and ZDT Functions Optimization Using Hybrid Micro Genetic Algorithm(HMGA)[J]. Soft Computing-a Fusion of Foundations, Methodologies and Applications, 2015, 19(12): 3571–3580 [Article] [Google Scholar]
  14. Li X. Better Spread and Convergence: Particle Swarm Multiobjective Optimization Using the Maximin Fitness Function[C]//Genetic and Evolutionary Computation Conference, 2004: 117–128 [Google Scholar]
  15. Peng H, Li R, Cao L L, et al. Multiple Swarms Multi-Objective Particle Swarm Optimization Based on Decomposition[J]. Procedia Engineering, 2011, 15(2): 3371–3375 [CrossRef] [Google Scholar]
  16. Peng X, Liu D, Shan J, et al. Airfoil Aerodynamic Optimization Based on an Improved Genetic Algorithm[C]//International Conference on Intelligent Systems Design & Engineering Applications, 2014: 133–137 [Google Scholar]
  17. Uoigne Alan Le, Qin NingVariable-Fidelity Aerodynamic Optimization for Turbulent Flows Using a Discrete Adjoint Formulation[J]. AIAA Journal, 2004, 42(42): 1281–1292 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  18. Laurenceau J, Meaux M, Montagnac M, et al. Comparison of Gradient-Based and Gradient-Enhanced Response-Surface-Based Optimizers[J]. AIAA Journal, 2010, 48(5): 981–994 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  19. Deb K, Pratap A, Agarwal S, et al. A Fast and Elitist Multi-Objective Genetic Algorithm:NSGA-Ⅱ[J]. IEEE Trans on Evolutionary Computation, 2002, 6(2): 182–197 [Article] [Google Scholar]

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