Agradecimentos: The authors would like to thank Dr. F.A.C. Viana for the prompt help with the SURROGATES Toolbox and also for the useful comments and discussions about the preliminary results of this work. W.G. Ferreira would like to thank Ford Motor Company and also the support of his colleagues at...

Agradecimentos: The authors would like to thank Dr. F.A.C. Viana for the prompt help with the SURROGATES Toolbox and also for the useful comments and discussions about the preliminary results of this work. W.G. Ferreira would like to thank Ford Motor Company and also the support of his colleagues at the MDO group and Product Development department that helped in the development of this work, which is part of his doctoral research concluded at UNICAMP by the end of 2016. Finally, the authors are grateful for the questions and comments from the journal editors and reviewers. Undoubtedly their valuable suggestions helped to improve the clarity and consistency of the present text

In this work we present LSEGO, an approach to drive efficient global optimization (EGO), based on LS (least squares) ensemble of metamodels. By means of LS ensemble of metamodels it is possible to estimate the uncertainty of the prediction with any kind of model (not only kriging) and provide an...

In this work we present LSEGO, an approach to drive efficient global optimization (EGO), based on LS (least squares) ensemble of metamodels. By means of LS ensemble of metamodels it is possible to estimate the uncertainty of the prediction with any kind of model (not only kriging) and provide an estimate for the expected improvement function. For the problems studied, the proposed LSEGO algorithm has shown to be able to find the global optimum with less number of optimization cycles than required by the classical EGO approach. As more infill points are added per cycle, the faster is the convergence to the global optimum (exploitation) and also the quality improvement of the metamodel in the design space (exploration), specially as the number of variables increases, when the standard single point EGO can be quite slow to reach the optimum. LSEGO has shown to be a feasible option to drive EGO with ensemble of metamodels as well as for constrained problems, and it is not restricted to kriging and to a single infill point per optimization cycle

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