Engineering design applications of surrogate-assisted optimization techniques

Ashill P, Fulker J (1987) Calculation of the viscous and vortex drag components of wing/body configurations. RAE Technical Report TR 87028

Brebner G, Wyatt L (1961) Boundary layer measurements at low speed on two wings of 45 and 55 degrees sweep. Tech. Rep. ARC-CP-554, Aeronautical Research Council

Cherkassky V, Mulier F (1998) Learning from data—concepts, theory, and methods. Wiley, New York

Chung H, Alonso J (2002) Using gradients to construct Cokriging approximation models for high-dimensional design optimization problems. In: AIAA 40th aerospace sciences meeting and exhibit, AIAA-2002-0317, Reno, NV

Coley D, Schukat S (2002) Low-energy design: combining computer-based optimisation and human judgement. Build Environ 37(12):1241–1247

Communities, Government L (2009) Improving the energy efficiency of our homes and buildings: energy certificates and air-conditioning inspections for buildings. http://www.communities.gov.uk/documents/planningandbuilding/pdf/714826.pdf . Accessed Dec 2009

Crawley D, Lawrie L, Winkelmann F, Buhl W, Huang Y, Pedersen C, Strand R, Liesen R, Fisher D, Witte M, Glazer J (2001) Energyplus: creating a new-generation building energy simulation program. Energy Build 33:319–331

Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA II. IEEE Trans Evol Comput 6(2):182–197

ESDU (2002) Full-potential (FP) method for three-dimensional wings and wing-body combinations—inviscid flow part 1: principles and results. ESDU-02013, London

ESDU (2008) Wing viscous drag coefficient in shock-free attached flow. ESDU-07002, London

Forrester AIJ, Keane AJ (2008) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1–3):50–79

Forrester AIJ, Sóbester A, Keane AJ (2007) Multi-fidelity optimization via surrogate modelling. Proc R Soc A 463(2088):3251–3269

Forrester AIJ, Sóbester A, Keane AJ (2008) Engineering design via surrogate modeling—a practical guide. Wiley, New York

Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, Berlin

Hicks RM, Henne PA (1978) Wing design by numerical optimization. J Aircr 15:407–412

Johnson ME, Moore LM, Ylvisaker D (1990) Minimax and maximin distance designs. J Stat Plan Inference 26:131–148

Jones DR (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21:345–383

Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13(4):455–492

Keane AJ (2006) Statistical improvement criteria for use in multiobjective design optimization. AIAA J 44(4):879–891

Kennedy MC, O’Hagan A (2000) Predicting the output from complex computer code when fast approximations are available. Biometrika 87(1):1–13

Krajewski WF (1987) Cokriging radar-rainfall and rain-gauge data. J Geophys Res, Atmos 92(D8):9571–9580

Krige DG (1951) A statistical approach to some basic mine valuation problems on the witwatersrand. J Chem Metall Min Soc S Afr 52(6):119–139

Kulfan BM (2008) Universal parametric geometry representation method. J Aircr 45(1):142–158. doi: 10.2514/1.29958

Lock R (1985) Prediction of the drag of wings at subsonic speeds by viscous/inviscid interaction techniques. Paper 10, AGARD Report 723

Matheron G (1963) Principles of geostatistics. Econ Geol 58:1246–1266

McKay MD, Conover WJ, Beckman RJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245

Morris MD, Mitchell TJ (1995) Exploratory designs for computational experiments. J Stat Plan Inference 43:381–402

Neymark J, Judkoff R, Knabe G, Le H, Rig M, Glass A, Zweifel G (2002) Applying the building energy simulation test (BESTEST) diagnostic method to verification of space conditioning equipment models used in whole-building energy simulation programs. Energy Build 34:917–931

Samareh J (2001) Survey of shape parameterization techniques for high-fidelity multidisciplinary shape optimization. AIAA J 29(5):877–884

Schonlau M (1997) Computer experiments and global optimization. Ph.D. thesis, University of Waterloo, Waterloo, Ontario, Canada

Simpson T, Mistree F (2001) Kriging models for global approximation in simulation-based multidisciplinary design optimization. AIAA J 39(12):2233–2241

Sóbester A (2009) Concise airfoil representation via case-based knowledge capture. AIAA J 47(5):1209–1218

Sóbester A, Leary SJ, Keane AJ (2003) On the design of optimization strategies based on global response surface approximation models. J Glob Optim 33(1):31–59

Toal D (2009) Proper orthogonal decomposition and Kriging strategies for design. Ph.D. thesis

Toal DJJ, Forrester AIJ, Bressloff NW, Keane AJ, Holden CME (2009) An adjoint for likelihood maximization. Proc R Soc A 465(2111):3267–3287. doi: 10.1098/rspa.2009.0096

USDE (2010) Energy plus simulation tool. Available to download at http://apps1.eere.energy.gov/buildings/energyplus/ . Accessed Mar 2010

Wang W, Zmeureanu R, Rivard H (2005) Applying multi-objective genetic algorithms in green building design optimization. Build Environ 40:1512–1525

Welch WJ, Buck RJ, Sacks J, Wynn HP, Mitchell TJ, Morris MD (1992) Screening, predicting, and computer experiments. Technometrics 34:15–25

Wright A, Loosemore H, Farmani R (2002) Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy Build 34:959–972