Alomoush, W. Cuckoo Search Algorithm based Dynamic Parameter Adjustment Mechanism for Solving Global Optimization Problems. Int. J. Appl. Eng. Res. 2019, 14, 4434–4440.
Alrosan, A.; Alomoush, W.; Norwawi, N.; Alswaitti, M.; Makhadmeh, S.N. An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation. Neural Comput. Appl. 2021, 33, 1671–1697. [CrossRef]
Coello, C.A.C.; Brambila, S.G.; Gamboa, J.F.; Tapia, M.G.C.; Gómez, R.H. Evolutionary multiobjective optimization: Open research areas and some challenges lying ahead. Complex Intell. Syst. 2020, 6, 221–236. [CrossRef]
Ma, L.; Huang, M.; Yang, S.; Wang, R.; Wang, X. An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-Objective Optimization. IEEE Trans. Cybern. 2021, 1–13. [CrossRef] [PubMed]
Alomoush, A.A.; Alsewari, A.A.; Alamri, H.S.; Zamli, K.Z.; Alomoush, W.; Younis, M.I. Modified Opposition Based Learning to Improve Harmony Search Variants Exploration. In Proceedings of the International Conference of Reliable Information and Communication Technology, Johor, Malaysia, 22 September 2019; pp. 279–287.
Alomoush, A.A.; Alsewari, A.R.A.; Zamli, K.Z.; Alrosan, A.; Alomoush, W.; Alissa, K. Enhancing three variants of harmony search algorithm for continuous optimization problems. Int. J. Electr. Comput. Eng. (IJECE) 2021, 11, 2343–2349. [CrossRef]
Alomoush, W.; Omar, K.; Alrosan, A.; Alomari, Y.M.; Albashish, D.; Almomani, A. Firefly photinus search algorithm. J. King Saud Univ.-Comput. Inf. Sci. 2020, 32, 599–607. [CrossRef]
Constantine, P.G. Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies; SIAM: Philadelphia, PA, USA, 2015.
Scott, D.W. The curse of dimensionality and dimension reduction. Multivar. Density Estim. Theory Pract. Vis. 2008, 1, 195–217.
Yao, W.; Chen, X.; Luo, W.; Van Tooren, M.; Guo, J. Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles. Prog. Aerosp. Sci. 2011, 47, 450–479. [CrossRef]
Leifsson, L.; Koziel, S. Multi-fidelity design optimization of transonic airfoils using physics-based surrogate modeling and shape-preserving response prediction. J. Comput. Sci. 2010, 1, 98–106. [CrossRef]
Li, W.; Krist, S.; Campbell, R. Transonic airfoil shape optimization in preliminary design environment. J. Aircr. 2006, 43, 639–651. [CrossRef]
Russi, T.M. Uncertainty Quantification with Experimental Data and Complex System Models; University of California: Berkeley, CA, USA, 2010.
Constantine, P.G.; Dow, E.; Wang, Q. Active subspace methods in theory and practice: Applications to kriging surfaces. SIAM J. Sci. Comput. 2014, 36, A1500–A1524. [CrossRef]
Constantine, P.G.; Zaharatos, B.; Campanelli, M. Discovering an active subspace in a single-diode solar cell model. Stat. Anal. Data Min. ASA Data Sci. J. 2015, 8, 264–273. [CrossRef]
Hu, X.; Chen, X.; Parks, G.T.; Tong, F. Uncertainty-based design optimization approach based on cumulative distribution matching. Struct. Multidiscip. Optim. 2019, 60, 1571–1582. [CrossRef]
Ma, L.; Li, N.; Guo, Y.; Wang, X.; Yang, S.; Huang, M.; Zhang, H. Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System. IEEE Trans. Cybern. 2021, 1–14. [CrossRef] [PubMed]
Zhu, Z.; Guo, H. Design of an RBF Surrogate Model for Low Reynolds Number Airfoil Based on Transfer Learning. In Proceedings of the 2019 Chinese Control and Decision Conference (CCDC), Nanchang, China, 3–5 June 2019; pp. 4555–4559.
Ma, Z.; Cui, B.; Wang, Z.; Zhao, D. Parameter Reduction of Composite Load Model Using Active Subspace Method. IEEE Trans. Power Syst. 2021, 36, 5441–5452. [CrossRef]
Wang, Q.; Chen, H.; Hu, R.; Constantine, P. Conditional sampling and experiment design for quantifying manufacturing error of transonic airfoil. In Proceedings of the 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, Orlando, FL, USA, 4–7 January 2011; p. 658.
Constantine, P.G. A quick-and-dirty check for a one-dimensional active subspace. arXiv 2014, arXiv:1402.3838.
Miller, S.J. The Method of Least Squares; Mathematics Department Brown University: Providence, RI, USA, 2006; Volume 8, pp. 1–7.
Hu, X.; Chen, X.; Parks, G.T.; Yao, W. Review of improved Monte Carlo methods in uncertainty-based design optimization for aerospace vehicles. Prog. Aerosp. Sci. 2016, 86, 20–27. [CrossRef]
Han, Z.-H.; Chen, J.; Zhang, K.-S.; Xu, Z.-M.; Zhu, Z.; Song, W.-P. Aerodynamic shape optimization of natural-laminar-flow wing using surrogate-based approach. AIAA J. 2018, 56, 2579–2593. [CrossRef]
Tang, G. Methods for High Dimensional Uncertainty Quantification: Regularization, Sensitivity Analysis, and Derivative Enhancement; Stanford University: Stanford, CA, USA, 2013.
Jun, T.; Gang, S.; Liqiang, G.; Xinyu, W. Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization. Chin. J. Aeronaut. 2020, 33, 1573–1588.
Broomhead, D.S.; Lowe, D. Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks; Royal Signals and Radar Establishment Malvern: Malvern, UK, 1988.
Dash, C.S.K.; Behera, A.K.; Dehuri, S.; Cho, S.-B. Radial basis function neural networks: A topical state-of-the-art survey. Open Comput. Sci. 2016, 6, 33–63. [CrossRef]
Feruglio, L.; Corpino, S. Neural networks to increase the autonomy of interplanetary nanosatellite missions. Robot. Auton. Syst. 2017, 93, 52–60. [CrossRef]
Hu, X.; Zhou, Z.; Chen, X.; Parks, G.T. Chance-constrained optimization approach based on density matching and active subspaces. AIAA J. 2018, 56, 1158–1169. [CrossRef]
Hwang, J.T. A Modular Approach to Large-Scale Design Optimization of Aerospace Systems; University of Michigan: Ann Arbor, MI, USA, 2015.
Weisstein, E. Least Squares Fitting—from Wolfram Math World. [Online], [Retrieved on 4 March 2013]. Retrieved from the Internet at, Last Updated 2 March 2013. Available online: https://mathworld.wolfram.com/LeastSquaresFitting.html (accessed on 6 May 2022).
Zhang, X.; Xie, F.; Ji, T.; Zhu, Z.; Zheng, Y. Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization. Comput. Methods Appl. Mech. Eng. 2021, 373, 113485. [CrossRef]
Wang, Y.; Han, Z.-H.; Zhang, Y.; Song, W.-P. Efficient global optimization using multiple infill sampling criteria and surrogate models. In Proceedings of the 2018 AIAA Aerospace Sciences Meeting, Kissimmee, FL, USA, 8–12 January 2018; p. 0555.
Wu, X.; Zhang, W.; Peng, X.; Wang, Z. Benchmark aerodynamic shape optimization with the POD-based CST airfoil parametric method. Aerosp. Sci. Technol. 2019, 84, 632–640. [CrossRef]
