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.

The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a linear antenna array is addressed within the Bayesian compressive sensing (BCS) framework. Unlike several state-of-the-art approaches, the voltages at the output of the receiving sensors are directly used to determine the DoAs of the signals thus avoiding the computation of the correlation matrix. Towards this end, the estimation problem is properly formulated to enforce the sparsity of the solution in the linear relationships between output voltages (i.e., the problem data) and the unknown DoAs. Customized implementations exploiting the measurements collected at a unique time instant (single-snapshot) and multiple time instants (multiple-snapshots) are presented and discussed. The effectiveness of the proposed approaches is assessed through an extensive numerical analysis addressing different scenarios, signal configurations, and noise conditions. Comparisons with state-of-the-art methods are reported, as well.

A numerically-efficient technique based on the Bayesian compressive sampling (BCS) for the design of maximally-sparse linear arrays is introduced. The method is based on a probabilistic formulation of the array synthesis and it exploits a fast relevance vector machine (RVM) for the problem solution. The proposed approach allows the design of linear arrangements fitting desired power patterns with a reduced number of non-uniformly spaced active elements. The numerical validation assesses the effectiveness and computational efficiency of the proposed approach as a suitable complement to existing state-of-the-art techniques for the design of sparse arrays.