Sequential Radial Basis Function-Based Optimization Method Using Virtual Sample Generation

[1]  Der-Chiang Li,et al.  Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge , 2007, Comput. Oper. Res..

[2]  Xiao-Shun Zhao,et al.  Fluid-structure interaction study of the supersonic parachute using large-eddy simulation , 2018 .

[3]  J. Angelier,et al.  Inversion of field data in fault tectonics to obtain the regional stress—III. A new rapid direct inversion method by analytical means , 1990 .

[4]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[5]  Yu Zhang,et al.  Weighted Gradient-Enhanced Kriging for High-Dimensional Surrogate Modeling and Design Optimization , 2017 .

[6]  G. G. Wang,et al.  Efficient adaptive response surface method using intelligent space exploration strategy , 2015 .

[7]  Jian Liu,et al.  Sequential Radial Basis Function Using Support Vector Machine for Expensive Design Optimization , 2017 .

[8]  Lei Ren,et al.  A review on topological architecture and design methods of cable-driven mechanism , 2018 .

[9]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[10]  N. Cressie.  The origins of kriging , 1990 .

[11]  T. Poggio,et al.  Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries , 1992 .

[12]  M. Kawato,et al.  New insights into olivo-cerebellar circuits for learning from a small training sample , 2017, Current Opinion in Neurobiology.

[13]  Wu Wen-rui.  Multi-Disciplinary Design Optimization of Ocean Satellites Based on Analytical Target Cascading Strategy , 2013 .

[14]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007 .

[15]  Martin D. Buhmann,et al.  Radial Basis Functions: Theory and Implementations: Preface , 2003 .

[16]  John E. Bussoletti,et al.  "Fundamental" Parameteric Geometry Representations for Aircraft Component Shapes , 2006 .

[17]  Der-Chiang Li,et al.  The attribute-trend-similarity method to improve learning performance for small datasets , 2017, Int. J. Prod. Res..

[18]  Yan-Lin He,et al.  A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries , 2017 .

[19]  Layne T. Watson,et al.  Efficient global optimization algorithm assisted by multiple surrogate techniques , 2012, Journal of Global Optimization.

[20]  Teng Long,et al.  Filter-based adaptive Kriging method for black-box optimization problems with expensive objective and constraints , 2019, Computer Methods in Applied Mechanics and Engineering.

[21]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[22]  Markus Clemens,et al.  Dimension Reduction for the Design Optimization of Large Scale High Voltage Devices Using Co-Kriging Surrogate Modeling , 2015, IEEE Transactions on Magnetics.

[23]  Huang Chong-fu,et al.  Principle of information diffusion , 1997 .

[24]  Kambiz Haji Hajikolaei,et al.  Employing partial metamodels for optimization with scarce samples , 2018 .

[25]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[26]  R. Regis.  Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points , 2014 .

[27]  Yanlin He,et al.  A PSO based virtual sample generation method for small sample sets: Applications to regression datasets , 2017, Eng. Appl. Artif. Intell..

[28]  G. G. Wang,et al.  Mode-pursuing sampling method for global optimization on expensive black-box functions , 2004 .

[29]  Liping Chen,et al.  J Glob Optim Table 1 Constraint handling in auxiliary problem for Kriging and radial basis function Surrogate model Constraint handling in auxiliary problem Via penalty Via explicit constraint Kriging Schonlau , 2016 .

[30]  G. Gary Wang,et al.  ADAPTIVE RESPONSE SURFACE METHOD - A GLOBAL OPTIMIZATION SCHEME FOR APPROXIMATION-BASED DESIGN PROBLEMS , 2001 .

[31]  Yan Li,et al.  A two-level global optimization method based on hybrid metamodel for expensive problems , 2018 .

[32]  Der-Chiang Li,et al.  Improving Virtual Sample Generation for Small Sample Learning with Dependent Attributes , 2016, 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI).

[33]  Der-Chiang Li,et al.  Using virtual sample generation to build up management knowledge in the early manufacturing stages , 2006, Eur. J. Oper. Res..

[34]  Fan Ye,et al.  Two-level Multi-surrogate Assisted Optimization method for high dimensional nonlinear problems , 2016, Appl. Soft Comput..

[35]  G. Gary Wang,et al.  Trust Region Based Mode Pursuing Sampling Method for Global Optimization of High Dimensional Design Problems , 2015 .

[36]  K. Schittkowski.  NLPQL: A fortran subroutine solving constrained nonlinear programming problems , 1986 .

[37]  Joaquim R. R. A. Martins,et al.  Gradient-enhanced kriging for high-dimensional problems , 2017, Engineering with Computers.

[38]  Teng Long,et al.  Filter-Based Sequential Radial Basis Function Method for Spacecraft Multidisciplinary Design Optimization , 2019, AIAA Journal.

[39]  Mohammad Rouhi,et al.  Computational efficiency and accuracy of multi-step design optimization method for variable stiffness composite structures , 2017 .

[40]  Rommel G. Regis,et al.  Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions , 2011, Comput. Oper. Res..

[41]  Yufei Wu,et al.  Dual-Sampling Based Co-Kriging Method for Design Optimization Problems with Multi-Fidelity Models , 2018, 2018 Multidisciplinary Analysis and Optimization Conference.

[42]  K. Gupta,et al.  Metamodel-Based Optimization for Problems With Expensive Objective and Constraint Functions , 2011 .

[43]  Wei Chen,et al.  A New Variable-Fidelity Optimization Framework Based on Model Fusion and Objective-Oriented Sequential Sampling , 2007, DAC 2007.

[44]  G. Gary Wang,et al.  Performance study of mode-pursuing sampling method , 2009 .

[45]  Shaojun Feng,et al.  Adaptive gradient-enhanced kriging model for variable-stiffness composite panels using Isogeometric analysis , 2018 .

[46]  Jian Liu,et al.  Gradient-Free Trust-Region-Based Adaptive Response Surface Method for Expensive Aircraft Optimization , 2017 .

[47]  Liang Gao,et al.  A multi-point sampling method based on kriging for global optimization , 2017 .

[48]  Matthew B. Parkinson,et al.  Improving an Ergonomics Testing Procedure via Approximation- based Adaptive Experimental Design , 2005 .

[49]  Long Teng,et al.  Discuss on Approximate Optimization Strategies Using Design of Computer Experiments and Metamodels for Flight Vehicle Design , 2016 .

[50]  Jianpei Zhang,et al.  A novel virtual sample generation method based on Gaussian distribution , 2011, Knowl. Based Syst..

[51]  Johan A. K. Suykens,et al.  LS-SVMlab Toolbox User's Guide version 1.7 , 2003 .

[52]  Alexander I. J. Forrester,et al.  Multi-fidelity optimization via surrogate modelling , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.