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 look space subset that contains a group of Pareto optimal solutions, i.e. those representing the most effective doable trade-offs between the conflicting objectives (like the antenna size and its electrical performance parameters). A fast response surface approximation (RSA) surrogate is subsequently created in a reduced search area using sampled coarse-discretisation electromagnetic (EM) simulation information. Owing to the authors’ reduction approach, the surrogate model construction is computationally possible even when the amount 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 illustration at the high-fidelity EM antenna model level). The approach is illustrated using 2 design cases. A comparison with previously published methods, as well as experimental validation, is additionally provided.

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