ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems

Audet, C., Bechard, V., Chaouki, J.: Spent potliner treatment process optimization using a MADS algorithm. Optim. Eng. 9(2), 143–160 (2008)

Bartholomew-Biggs, M.C., Parkhurst, S.C., Wilson, S.P.: Using DIRECT to solve an aircraft routing problem. Comput. Optim. Appl. 21(3), 311–323 (2002)

Boukouvala, F., Ierapetritou, M.G.: Surrogate-based optimization of expensive flowsheet modeling for continuous pharmaceutical manufacturing. J. Pharm. Innov. 8(2), 131–145 (2013)

Caballero, J.A., Grossmann, I.E.: An algorithm for the use of surrogate models in modular flowsheet optimization. Aiche J. 54(10), 2633–2650 (2008)

Egea, J.A., Rodriguez-Fernandez, M., Banga, J.R., Marti, R.: Scatter search for chemical and bio-process optimization. J. Global Optim. 37(3), 481–503 (2007)

Fahmi, I., Cremaschi, S.: Process synthesis of biodiesel production plant using artificial neural networks as the surrogate models. Comput. Chem. Eng. 46, 105–123 (2012)

Fowler, K.R., Reese, J.P., Kees, C.E., Dennis Jr., J.E., Kelley, C.T., Miller, C.T., Audet, C., Booker, A.J., Couture, G., Darwin, R.W., Farthing, M.W., Finkel, D.E., Gablonsky, J.M., Gray, G., Kolda, T.G.: Comparison of derivative-free optimization methods for groundwater supply and hydraulic capture community problems. Adv. Water Resour. 31(5), 743–757 (2008)

Graciano, J.E.A., Roux, G.A.C.L.: Improvements in surrogate models for process synthesis. Application to water network system design. Comput. Chem. Eng. 59, 197–210 (2013)

Hemker, T., Fowler, K., Farthing, M., Stryk, O.: A mixed-integer simulation-based optimization approach with surrogate functions in water resources management. Optim. Eng. 9(4), 341–360 (2008)

Henao, C.A., Maravelias, C.T.: Surrogate-based superstructure optimization framework. AIChE J. 57(5), 1216–1232 (2011)

Kleijnen, J.P.C., van Beers, W., van Nieuwenhuyse, I.: Constrained optimization in expensive simulation: novel approach. Eur. J. Oper. Res. 202(1), 164–174 (2010)

Wan, X.T., Pekny, J.F., Reklaitis, G.V.: Simulation-based optimization with surrogate models—application to supply chain management. Comput. Chem. Eng. 29(6), 1317–1328 (2005)

Espinet, A., Shoemaker, C., Doughty, C.: Estimation of plume distribution for carbon sequestration using parameter estimation with limited monitoring data. Water Resour. Res. 49(7), 4442–4464 (2013)

Hasan, M.M.F., Baliban, R.C., Elia, J.A., Floudas, C.A.: Modeling, simulation, and optimization of postcombustion CO$$_{2}$$ capture for variable feed concentration and flow rate. 2. Pressure swing adsorption and vacuum swing adsorption processes. Ind. Eng. Chem. Res. 51(48), 15665–15682 (2013)

Hasan, M.M.F., Boukouvala, F., First, E.L., Floudas, C.A.: Nationwide, regional, and statewide $$CO_{2}$$ capture, utilization, and sequestration supply chain network optimization. Ind. Eng. Chem. Res. 53(18), 7489–7506 (2014)

Li, S., Feng, L., Benner, P., Seidel-Morgenstern, A.: Using surrogate models for efficient optimization of simulated moving bed chromatography. Comput. Chem. Eng. 67, 121–132 (2014)

Forrester, A.I.J., Sóbester, A., Keane, A.J.: Engineering Design via Surrogate Modelling—A Practical Guide. Wiley, Chichester (2008)

Conn, A.R., Scheinberg, K., Vicente, L.N.: Introduction to Derivative-Free Optimization. MPS-SIAM Series on Optimization, vol. 8. SIAM, Philadelphia (2009)

Martelli, E., Amaldi, E.: PGS-COM: a hybrid method for constrained non-smooth black-box optimization problems: brief review, novel algorithm and comparative evaluation. Comput. Chem. Eng. 63, 108–139 (2014)

Rios, L.M., Sahinidis, N.V.: Derivative-free optimization: a review of algorithms and comparison of software implementations. J. Global Optim. 56(3), 1247–1293 (2013)

Boukouvala, F., Misener, R., Floudas, C.A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO. Eur. J. Oper. Res. 252, 701–727(2016) (in press). doi: 10.1016/j.ejor.2015.12.018

Bjorkman, M., Holmstrom, K.: Global optimization of costly nonconvex functions using radial basis functions. Optim. Eng. 1(4), 373–397 (2000)

Booker, A.J., Dennis, J.E., Frank, P.D., Serafini, D.B., Torczon, V., Trosset, M.W.: A rigorous framework for optimization of expensive functions by surrogates. Struct. Multidiscip. Optim. 17(1), 1–13 (1999)

Boukouvala, F., Ierapetritou, M.G.: Derivative-free optimization for expensive constrained problems using a novel expected improvement objective function. AIChE J. 60(7), 2462–2474 (2014)

Conn, A.R., Le Digabel, S.: Use of quadratic models with mesh-adaptive direct search for constrained black box optimization. Optim. Methods Softw. 28(1), 139–158 (2013)

Forrester, A.I.J., Keane, A.J.: Recent advances in surrogate-based optimization. Prog. Aerosp. Sci. 45(1), 50–79 (2009)

Jakobsson, S., Patriksson, M., Rudholm, J., Wojciechowski, A.: A method for simulation based optimization using radial basis functions. Optim. Eng. 11(4), 501–532 (2010)

Jones, D.R.: A taxonomy of global optimization methods based on response surfaces. J. Global Optim. 21(4), 345–383 (2001)

Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455–492 (1998)

Regis, R.G.: Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points. Eng. Optim. 46(2), 218–243 (2014)

Regis, R.G., Shoemaker, C.A.: Constrained global optimization of expensive black box functions using radial basis functions. J. Global Optim. 31(1), 153–171 (2005)

Yao, W., Chen, X.Q., Huang, Y.Y., van Tooren, M.: A surrogate-based optimization method with RBF neural network enhanced by linear interpolation and hybrid infill strategy. Optim. Methods Softw. 29(2), 406–429 (2014)

Muller, J., Shoemaker, C.A.: Influence ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization methods. J. Glob. Optim. 60(2), 123–144 (2014)

Viana, F.A.C., Haftka, R.T., Watson, L.T.: Efficient global optimization algorithm assisted by multiple surrogate techniques. J. Glob. Optim. 56(2), 669–689 (2013)

Floudas, C.A.: Deterministic Global Optimization, vol. 37. Springer Science & Business Media, New York (1999)

Floudas, C.A., Gounaris, C.E.: A review of recent advances in global optimization. J. Global Optim. 45(1), 3–38 (2009)

Boukouvala, F., Hasan, M.M.F., Christodoulos, A.F.: Global optimization of constrained grey-box problems: new method and application to constrained PDEs for pressure swing adsorption. J. Global Optim. (2015). doi: 10.1007/s10898-015-0376-2

Misener, R., Floudas, C.: ANTIGONE: algorithms for continuous/integer global optimization of nonlinear equations. J. Global Optim. 59(2–3), 503–526 (2014)

Maranas, C.D., Floudas, C.A.: Finding all solutions of nonlinearly constrained systems of equations. J. Global Optim. 7(2), 143–182 (1995)

Misener, R., Floudas, C.A.: GloMIQO: global mixed-integer quadratic optimizer. J. Global Optim. 57(1), 3–50 (2013)

Li, Z., Floudas, C.A.: Optimal scenario reduction framework based on distance of uncertainty distribution and output performance: I. Single reduction via mixed integer linear optimization. Comput. Chem. Eng. 70, 50–65 (2014)

Powell, M.J.D.: Approximation Theory and Methods. Cambridge University Press (1981)

Holmstrom, K., Quttineh, N.-H., Edvall, M.M.: An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization. Optim. Eng. 9(4), 311–339 (2008)

Kleijnen, J.P.C.: Kriging metamodeling in simulation: a review. Eur. J. Oper. Res. 192(3), 707–716 (2009)

Adjengue, L., Audet, C., Ben Yahia, I.: A variance-based method to rank input variables of the mesh adaptive direct search algorithm. Optim. Lett. 8(5), 1599–1610 (2014). doi: 10.1007/s11590-013-0688-4

Fan, J., Lv, J.: Sure independence screening for ultrahigh dimensional feature space. J. R. Stat. Soc. Ser. B (Statistical Methodology) 70(5), 849–911 (2008). doi: 10.1111/j.1467-9868.2008.00674.x

Fan, J., Song, R.: Sure independence screening in generalized linear models with NP-dimensionality. Ann. Stat. 38(6) 3567–3604 (2010). doi: 10.1214/10-AOS798

Fan, J., Feng, Y., Saldana, D.F., Samworth, R., Wu, Y.: Sure independence screening version 0.7-5 (2015). https://cran.r-project.org/web/packages/SIS/index.html

Misener, R., Floudas, C.A.: Global optimization of mixed-integer models with quadratic and signomial functions: a review. Appl. Comput. Math. 11, 317–336 (2012)

Floudas, C.A., Pardalos, P.M., Adjiman, C.S., Esposito, W.R., Gumus, Z.H., Harding, S.T., Klepeis, J.L., Meyer, C.A., Schweiger, C.A.: Handbook of Test Problems in Local and Global Optimization. Kluwer Academic Publishers, Berlin (1999)

Abramson, M.A., Audet, C., Couture, G., Dennis, J.E., S, Tribes, C.: The NOMAD Project (2009). https://www.gerad.ca/nomad

Le Digabel, S.: Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm. ACM Trans. Math. Softw. (TOMS) 37(4), 1–15 (2011)

Runarsson, T.P., Xin, Y.: Search biases in constrained evolutionary optimization. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35(2), 233–243 (2005). doi: 10.1109/TSMCC.2004.841906

Johnson, S.G.: The NLopt nonlinear-optimization package (2015). http://ab-initio.mit.edu/nlopt

Powell, M.J.D.: A Direct Search Optimization Method that Models the Objective and Constraint Functions by Linear Interpolation. In: Gomez, S., Hennart, J.-P. (eds.) Advances in Optimization and Numerical Analysis. Mathematics and Its Applications, vol. 275, pp. 51–67. Springer, Netherlands (1994)