Chapter 5: Surrogate-Based Modeling and Optimization

In the context of electromagnetic (EM)-simulation-driven design of antenna structures, the major disadvantage of conventional numerical optimization techniques outlined in Chapters 3 and 4 is their high computational cost, typically measured in hundreds (for local methods) or thousands and tens of thousands of objective function evaluations (for population-based metaheuristics). The exception is gradient-based search with adjoint sensitivities (Ghassemi et al., 2013; Koziel and Ogurtsov, 2012c; Koziel et al., 2013c, 2014d; Koziel and Bekasiewicz, 2015a, 2016f), where the optimization process can be conducted in reasonable time even for relatively large number of designable parameters. Nevertheless, it seems that the most promising approaches in terms of expedited simulation-driven design are those exploiting surrogate models. In this chapter, we provide a brief introduction to surrogate-based optimization (SBO). In particular, we outline the SBO concept, discuss various surrogate modeling techniques, as well as overview surrogate-assisted optimization methods both approximation- and physics-based. The algorithms for cost-efficient multi-objective optimization of antenna structures considered later in this book are largely based on SBO paradigm and exploit specific SBO methods as the building blocks of the antenna design frameworks.