Fast multi-objective surrogate-assisted design of multi-parameter antenna structures through rotational design space reduction

A technique for fast multi-objective optimisation of antennas is introduced. The core of the proposed methodology is a reliable initial estimation of the design space subset that contains a set of Pareto optimal solutions, i.e. those representing the best possible trade-offs between the conflicting objectives (such as the antenna size and its electrical performance parameters). A fast response surface approximation (RSA) surrogate is subsequently constructed in a reduced search space using sampled coarse-discretisation electromagnetic (EM) simulation data. Owing to the authors' reduction approach, the surrogate model construction is computationally feasible even when the number of antenna parameters is high. The RSA model is optimised using a multi-objective evolutionary algorithm to yield an initial approximation of the Pareto set. The latter is further refined (to obtain its representation at the highfidelity EM antenna model level). The approach is illustrated using two design cases. A comparison with previously published methods, as well as experimental validation, is also provided.