Evolution of nature-inspired optimization for new generation antenna design

The use of nature-inspired optimization strategies based computational intelligence, like Evolutionary Algorithms (EAs), has had a revolutionary impact in various frameworks of electromagnetics since has enabled the design of complex structures (e.g., antenna arrays) with improved performance. The main issues that still remain are related to the high computational costs and the non-efficient sampling of the solution space which limit convergence rate and the possibility to retrieve optimal solutions. To address these drawbacks, several research efforts are currently dedicated to the development of hybrid optimization procedures where sub-optimal solutions, easily defined by means of either analytic or deterministic techniques, are used as starting guess or the search spaces are suitably re-defined to enable the use of state-of-the-art EAs. Two representative examples are revised and discussed in this paper aimed to the design of antenna arrays generating compromise sum-difference patterns on the same antenna aperture and of large thinned arrays.