J Glob Optim (2013) 56:669–689 DOI 10.1007/s10898-012-9892-5 Efficient global optimization algorithm assisted by multiple surrogate techniques Felipe A. C. Viana · Raphael T. Haftka · Layne T. Watson Received: 13 December 2011 / Accepted: 13 March 2012 / Published online: 24 March 2012 © Springer Science+Business Media, LLC. 2012 Abstract Surrogate-based optimization proceeds in cycles. Each cycle consists of analyz- ing a number of designs, fitting a surrogate, performing optimization based on the surrogate, and finally analyzing a candidate solution. Algorithms that use the surrogate uncertainty estimator to guide the selection of the next sampling candidate are readily available, e.g., the efficient global optimization (EGO) algorithm. However, adding one single point at a time may not be efficient when the main concern is wall-clock time (rather than number of simulations) and simulations can run in parallel. Also, the need for uncertainty estimates limits EGO-like strategies to surrogates normally implemented with such estimates (e.g., kriging and polynomial response surface). We propose the multiple surrogate efficient global optimization (MSEGO) algorithm, which adds several points per optimization cycle with the help of multiple surrogates. We import uncertainty estimates from one surrogate to another to allow use of surrogates that do not provide them. The approach is tested on three analytic examples for nine basic surrogates including kriging, radial basis neural networks, linear Shepard, and six different instances of support vector regression. We found that MSEGO works well even with imported uncertainty estimates, delivering better results in a fraction of the optimization cycles needed by EGO. F. A. C. Viana (B ) · R. T. Haftka Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA e-mail: felipeacviana@gmail.com R. T. Haftka e-mail: haftka@ufl.edu Present Address: F. A. C. Viana GE Global Research, Niskayuna, NY 12309, USA L. T. Watson Departments of Computer Science and Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA e-mail: ltw@cs.vt.edu 123