Open Access
Volume 37, Number 5, October 2019
Page(s) 918 - 927
Published online 14 January 2020
  1. Kim S D. Aerodynamic Design of a Supersonic Inlet with a Parametric Bump[J]. Journal of Aircraft, 2009, 46(1): 198–203 [Article] [CrossRef] [Google Scholar]
  2. Lim S, Koh D H, Kim S D, et al. A Computational Study on the Efficiency of Boundary Layer Bleeding for the Supersonic Bump Type Inlet[C]//47th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 2009: 5–8 [Google Scholar]
  3. Gaiddon AKnight D D. Multicriteria Design Optimization of Integrated Three-Dimensional Supersonic Inlets[J]. Journal of Propulsion and Power, 2003, 19(3): 456–463 [Article] [CrossRef] [Google Scholar]
  4. Zhang Z K, Lum K Y. S-Shaped Inlet Design Optimization Using the Adjoint Equation Method[R]. AIAA-2006-4453 [Google Scholar]
  5. Lee C HAhn J KBae H G, et al. Efficient Global Optimization Incorporating Feasibility Criterion for the Design of a Supersonic Inlet[J]. Journal of Aerospace Engineering, 2012, 226(11): 1362–1372 [Article] [Google Scholar]
  6. Kim HLiou M S. Shape Design Optimization of Embedded Engine Inlets for N2B Hybrid Wing-Body Configuration[J]. Aerospace Science and Technology, 2013, 30128–149 [Article] [CrossRef] [Google Scholar]
  7. Gan WZhang X. Design Optimization of a Three-Dimensional Diffusing S-Duct Using a Modified SST Turbulent Model[J]. Aerospace Science and Technology, 2017, 63(4): 63–78 [Article] [CrossRef] [Google Scholar]
  8. Zhang PengfeiGong ShuxiXu Xueyun, et al Computation and Control of RCS for Large Open-Ended Cavities[J]. Journal of Xidian University, 2009, 36(1): 111–115 [Article] (in Chinese) [Google Scholar]
  9. Guo WenyanZhu Yanzhen. HF Electromagnetic Scattering Properties and RCS Calculation of the Airplane's Inlet[J]. Journal of Harbin Institute of Technology, 1999(1): 123–126 [Article] (in Chinese) [Google Scholar]
  10. Coello C A C, Lechuga M S. MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization[C]//Proceedings of the IEEE Congress on Evolutionary Computation, 2002: 1051–1056 [Google Scholar]
  11. Raquel C R, Naval P C. An Effective Use of Crowding Distance in Multi-Objective Particle Swarm Optimization[C]//Proceedings of the Workshops on Genetic and Evolutionary Computation, 2005: 257–264 [Google Scholar]
  12. Deb KPratap AAgarwal Set alA Fast and Elitist Multi-Objective Genetic Algorithm:NSGA-Ⅱ[J]. IEEE Trans on Evolutionary Computation, 2002, 6(2): 182–197 [Article] [CrossRef] [Google Scholar]
  13. Weron A, Weron R. Computer Simulation of Levy-α Stable Variables and Processes[M]. Springer Berlin Heidelberg, 1995: 379–392 [Google Scholar]
  14. Kogon S MManolakis D G. Signal Modeling with Self-Similarα Stable Processes:the Fractional Levy Stable Motion Model[J]. IEEE Trans on Signal Processing, 1996, 44(4): 1006–1010 [NASA ADS] [CrossRef] [Google Scholar]
  15. Fan HuayuZhan HaoCheng Shixinet alResearch and Application of Multi-Objective Particle Swarm Optimization Algorithm Based on α-Stable Distribution[J]. Journal of Northwestern Polytechnical University, 2019, 37(2): 232–241 [Article] (in Chinese) [CrossRef] [Google Scholar]
  16. Emmerich MGiannakoglou KNaujoks B. Single-and Multi-Objective Evolutionary Optimization Assisted by Gaussian Random Field Metamodels[J]. IEEE Trans on Evolutionary Computation, 2006, 10(4): 421–439 [Article] [CrossRef] [Google Scholar]
  17. Emmerich M, Deutz A H, Klinkenberg J W. Hypervolume-Based Expected Improvement: Monotonicity Properties and Exact Computation[C]//IEEE Congress on Evolutionary Computation, 2011: 2147–2154 [Google Scholar]
  18. Zitzler EEvolutionary Algorithms for Multiobjective Optimization[M]. Switzerland, Zurich, Swiss Federal Institute of Technology, 1998 [Google Scholar]
  19. Jones D RSchonlau MWelch W J. Efficient Global Optimization of Expensive Black-Box Functions[J]. Journal of Global Optimization, 1998, 13455–492 [Article] [CrossRef] [Google Scholar]
  20. Cheng ShixinZhan HaoShu Zhaoxinet alEffective Optimization on Bump Inlet Using Meta-Model Multi-Objective Particle Swarm Assisted by Expected Hyper-Volume Improvement[J]. Aerospace Science and Technology, 2019, 87431–447 [Article] [CrossRef] [Google Scholar]
  21. Cheng S XZhan HShu Z X. An Innovative Hybrid Multi-Objective Particle Swarm Optimization with or without Constraints Handling[J]. Applied Soft Computing, 2016, 47370–388 [Article] [CrossRef] [Google Scholar]
  22. Menter F R. Two-Equation Eddy-Viscosity Turbulence Models for Engineering Applications[J]. AIAA Journal, 1994, 32(8): 1598–1605 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  23. Wang Xu, Song Bifeng, Guo Xiaohui. Research on the Approach for Calculating the Probability of Detecting an Aircraft by Radar System[J]. Systems Engineering-Theory & Practice (in Chinese) [Google Scholar]
  24. Zhang Kao Ma Dongli Military Aircraft Survivability and Stealt Design Beijing National Defense Industry Press 2002 (in Chinese) [Google Scholar]
  25. Morris M DMitchell T J. Exploratory Designs for Computer Experiments[J]. Journal of Statistical Planning and Inference, 1995, 43(3): 381–402 [Article] [CrossRef] [Google Scholar]

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