A Kriging-Based Unconstrained Global Optimization Algorithm

[1]Li, Y., Y. Wu, and Z. Huang, “An incremental kriging method for sequential optimal experimental design”, CMES - Computer Modeling in Engineering and Sciences, Vol.97, No.4, 2014, pp. 323-357. Search in Google Scholar

[2]Myers, R.H. and D.C. Montgomery, “Response Surface Methodology-Process and Product Optimization Using Designed Experiments”, 2009: WILEY. Search in Google Scholar

[3]Kleijnen, J.P.C., “Kriging metamodeling in simulation: A review”, European Journal of Operational Research, Vol.192, No.3, 2009, pp. 707-716.10.1016/j.ejor.2007.10.013 Search in Google Scholar

[4]Jones, D.R., M. Schonlau, and W.J. Welch, “Efficient global optimization of expensive black-box functions”, Journal of Global optimization, Vol.13, No.4, 1998, pp. 455-492.10.1023/A:1008306431147 Search in Google Scholar

[5]Kanazaki, M., H. Takagi, and Y. Makino, “Mixed-fidelity Efficient Global Optimization Applied to Design of Supersonic Wing”, Procedia Engineering, Vol.67, No.0, 2013, pp. 85-99.10.1016/j.proeng.2013.12.008 Search in Google Scholar

[6]Horowitz, B., et al., “A concurrent efficient global optimization algorithm applied to polymer injection strategies”, Journal of Petroleum Science and Engineering, Vol.71, No.3, 2010, pp. 195-204.10.1016/j.petrol.2010.02.002 Search in Google Scholar

[7]Ur Rehman, S., M. Langelaar and F. van Keulen, “Efficient Kriging-based robust optimization of unconstrained problems”, Journal of Computational Science. Search in Google Scholar

[8]Parr, J.M., et al., “Infill sampling criteria for surrogate-based optimization with constraint handling”, Engineering Optimization, Vol.44, No.10, 2012, pp. 1147-1166.10.1080/0305215X.2011.637556 Search in Google Scholar

[9]Sakata, S., F. Ashida, and M. Zako, “Structural optimization using Kriging approximation. Computer Methods in Applied Mechanics and Engineering”, Vol.192, No.7, 2003, pp. 923-939.10.1016/S0045-7825(02)00617-5 Search in Google Scholar

[10]Jones, D.R., “A taxonomy of global optimization methods based on response surfaces”, Journal of Global Optimization, Vol.21, No.4, 2001, pp. 345-383.10.1023/A:1012771025575 Search in Google Scholar

[11]Seulgi, Y., K. Hyung-Il, and C. Seongim, “Efficient Global Optimization Using a Multi-objective Approach Infill Sampling Criteria”, in 52nd AIAA Aerospace Sciences Meeting, 13-17 Jan. 2014. 2013. Reston, VA, USA: American Institute of Aeronautics and Astronautics, Inc. Search in Google Scholar

[12]Huang, D., et al., “Global optimization of stochastic black-box systems via sequential kriging meta-models”, Journal of Global Optimization, Vol.34, No.3, 2006, pp. 441-466.10.1007/s10898-005-2454-3 Search in Google Scholar

[13]Chaudhuri, A., R.T. Haftka, and F.A.C. Viana,”Efficient global optimization with adaptive target for probability of targeted improvement”, in 53rd AIAA/ ASME/ ASCE/ AHS/ASC Structures, Structural Dynamics and Materials Conference 2012, April 23, 2012 - April 26, 2012. 2012. Honolulu, HI, United states: American Institute of Aeronautics and Astronautics Inc.10.2514/6.2012-1681 Search in Google Scholar

[14]Schonlau, M., W.J. Welch, and D.R. Jones, Global versus local search in constrained optimization of computer models, in New developments and applications in experimental design, N. Flournoy, W.F. Rosenberger, and W.K. Wong, Editors. 1998, Institute of Mathematical Statistics: Hayward, CA. pp. 11-25. Search in Google Scholar

[15]Sasena, M.J., P. Papalambros, and P. Goovaerts, Exploration of metamodeling sampling criteria for constrained global optimization. Engineering Optimization, Vol.34, No.3, 2002, pp. 263-278.10.1080/03052150211751 Search in Google Scholar

[16]Ponweiser, W., et al., Clustered Multiple Generalized Expected Improvement: A Novel Infill Sampling Criterion for Surrogate Models. 2008 Ieee Congress on Evolutionary Computation, Vols 1-8. 2008. pp. 3515-3522. Search in Google Scholar

[17]Jia, G. and A.A. Taflanidis, Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment. Computer Methods in Applied Mechanics and Engineering, Vol.261-262, 2013, PP.24-38.10.1016/j.cma.2013.03.012 Search in Google Scholar

[18]Lophaven, S.N., H.B. Nielsen, and J. Søndergaard, Aspects of the matlab toolbox DACE. 2002, Informatics and Mathematical Modelling, Technical University of Denmark, DTU. Search in Google Scholar

[19]HollandJohn, H., Adaptation in natural and artificail systerms. Ann Arbor: University of Michigan Press, 1975. Search in Google Scholar

[20]Gu, J., G.Y. Li, and Z. Dong, “Hybrid and adaptive meta-model-based global optimization”, Engineering Optimization, Vol.44, No.1, 2012, pp. 87-104.10.1080/0305215X.2011.564768 Search in Google Scholar

[21]MAO, H.-y., et al., “Design and calculation for oil chambers of cycloidal rotor pump”, Journal of Shandong University (Engineering Science), Vol.32, No.2, 2002. pp. 175-176. Search in Google Scholar