In electromagnetics, optimization problems generally require high computational resources and involve a large number of unknowns. They are usually characterized by non-convex functionals and continuous spaces suitable for strategies based on Differential Evolution (DE). In such a framework, this paper is aimed at presenting an overview of Differential Evolution-based approaches used in electromagnetics, pointing out novelties and customizations with respect to other fields of application. Starting from a general description of the evolutionary mechanism of Differential Evolution, Differential Evolution-based techniques for electromagnetic optimization are presented. Some hints on the convergence properties and the sensitivity to control parameters are also given. Finally, a comprehensive coverage of different Differential Evolution formulations in solving optimization problems in the area of computational electromagnetics is presented, focusing on antenna synthesis and inverse scattering.

The proliferation of wireless services is driving innovative phased array solutions that are able to provide better cost/performance tradeoffs. In this context, the use of irregular array architectures provides a viable solution. This paper reviews and highlights some of the most recent advances in this field, including clustered, thinned, sparse, and time-modulated arrays, and their proposed design methodologies.

Several problems arising in electromagnetics can be directly formulated or suitably recast for an effective solution within the compressive sensing (CS) framework. This has motivated a great interest in developing and applying CS methodologies to several conventional and innovative electromagnetic scenarios. This work is aimed at presenting, to the best of the authors' knowledge, a review of the state-of-the-art and most recent advances of CS formulations and related methods as applied to electromagnetics. Toward this end, a wide set of applicative scenarios comprising the diagnosis and synthesis of antenna arrays, the estimation of directions of arrival, and the solution of inverse scattering and radar imaging problems are reviewed. Current challenges and trends in the application of CS to the solution of traditional and new electromagnetic problems are also discussed.

In this paper, the synthesis of simultaneous multibeams through time-modulated linear arrays is studied. Unlike classical phased arrays where the antenna aperture is usually shared to generate multiple beams, the periodic on-off sequences controlling the static excitations are properly defined by means of an optimization strategy based on the Particle Swarm algorithm to afford desired multiple patterns at harmonic frequencies to make practical application of these harmonic beams which are typically regarded as an undesirable effect in time-modulated arrays. The synthesis of simultaneous broadside sum and difference patterns, flat-top and narrow beam patterns, and steered multibeams is enabled as assessed by a set of selected results reported and discussed to show the potentialities of the proposed method. Comparisons with previously published results are reported, as well.

A review of the most recent advances in deep learning (DL) as applied to electromagnetics (EM), antennas, and propagation is provided. It is aimed at giving the interested readers and pratictioners in EM and related applicative fields some useful insights on the effectiveness and the potentialities of deep neural networks (DNNs) as computational tools with an unprecedented computational efficiency. The range of considered applications includes forward/inverse scattering, direction-of-arrival (DoA) estimation, radar and remote sensing (RS), and multi-input/multi-output (MIMO) systems. Appealing DNN-based solutions concerned with localization, human behavior monitoring (HBM), and EM compatibility (EMC) are reported, as well. Some final remarks are drawn along with indications on future trends according to the authors' viewpoint.