Domain segmentation for low‐cost surrogate‐assisted multi‐objective design optimisation of antennas

Abstract

Information regarding the best possible design trade-offs of an antenna structure can be obtained through multi-objective optimisation (MO). Unfortunately, MO is extremely challenging if full-wave electromagnetic (EM) simulation models are used for performance evaluation. Yet, for the majority of contemporary antennas, EM analysis is the only tool that ensures reliability. This study introduces a procedure for accelerated MO design of antennas that exploits cheap data-driven surrogates, as well as MO evolutionary algorithm to yield an initial approximation of the Pareto set. The final Pareto set is found using output space mapping. The major contribution of this work is a further reduction of the computational cost of surrogate model construction using a design space segmentation mechanism, where a part of the space containing the Pareto front is first identified by means of single-objective optimisation runs and subsequently represented by a set of adjacent compartments with separate surrogate models established within them. Segmentation allows for significant reduction of the number of training points necessary to build a reliable surrogate and thus the overall optimisation cost. The presented technique is demonstrated using two antenna structures and compared with a state-of-the-art surrogate-assisted techniques. Experimental validation is also provided.