Implicit space mapping with adaptive selection of preassigned parameters

Implicit space mapping is one of the latest developments in space mapping (SM) technology. Its advantage is that the variables (the so-called preassigned parameters) used to adjust the surrogate model to have it match the fine model are typically separate from the design variables. Also, implicit space mapping offers greater flexibility in creating and enhancing surrogate models. Still, choosing the proper model as well as the right set of preassigned parameters - both being critical for the performance of the space mapping algorithm - is an open problem. Here, the authors apply suitable assessment techniques that help in automatically making the right selection of the model and, consequently, its associated parameters. The assessment is embedded into the SM algorithm so that the choice of the most suitable model is performed before each iteration of the algorithm. Our approach is verified using several microwave design optimisation problems. The authors also present a modified version of the adaptive SM to improve performance. Our examples are repeated using the modified adaptive SM and compared with the basic adaptive SM.