Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

A review of Internet of Things for smart home: Challenges and solutions

Initial efforts on wireless power transfer (WPT) have concentrated toward long-distance transmission and high power applications. Nonetheless, the lower achievable transmission efficiency and potential health concerns arising due to high power applications, have caused limitations in their further developments. Due to tremendous energy consumption growth with ever-increasing connected devices, alternative wireless information and power transfer techniques have been important not only for theoretical research but also for the operational costs saving and for the sustainable growth of wireless communications. In this regard, radio frequency energy harvesting (RF-EH) for a wireless communications system presents a new paradigm that allows wireless nodes to recharge their batteries from the RF signals instead of fixed power grids and the traditional energy sources. In this approach, the RF energy is harvested from ambient electromagnetic sources or from the sources that directionally transmit RF energy for EH purposes. Notable research activities and major advances have occurred over the last decade in this direction. Thus, this paper provides a comprehensive survey of the state-of-art techniques, based on advances and open issues presented by simultaneous wireless information and power transfer (SWIPT) and WPT assisted technologies. More specifically, in contrast to the existing works, this paper identifies and provides a detailed description of various potential emerging technologies for the fifth generation communications with SWIPT/WPT. Moreover, we provide some interesting research challenges and recommendations with the objective of stimulating future research in this emerging domain.

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Simultaneous Wireless Information and Power Transfer (SWIPT): Recent Advances and Future Challenges

Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications

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.

Differential Evolution as Applied to Electromagnetics

This review is aimed at presenting an overview of evolutionary algorithms (EAs) as applied to the solution of inverse scattering problems. The focus of this work is on the use of different population-based optimization algorithms for the reconstruction of unknown objects embedded in an inaccessible region when illuminated by a set of microwaves. Starting from a general description of the structure of EAs, the classical stochastic operators responsible for the evolution process are described. The extension to hybrid implementations when integrated with local search techniques and the exploitation of the 'domain knowledge', either a priori obtained or collected during the optimization process, are also presented. Some theoretical discussions concerned with the convergence issues and a sensitivity analysis on the parameters influencing the stochastic process are reported as well. Successively, a review on how various researchers have applied or customized different evolutionary approaches to inverse scattering problems is carried out ranging from the shape reconstruction of perfectly conducting objects to the detection of the dielectric properties of unknown scatterers up to applications to sub-surface or biomedical imaging. Finally, open problems and envisaged developments are discussed.

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Evolutionary optimization as applied to inverse scattering problems

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.

Unconventional Phased Array Architectures and Design Methodologies—A Review

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.

Compressive Sensing in Electromagnetics - A Review

The minimization of the power losses in time-modulated arrays is addressed by means of a suitable strategy based on particle swarm optimization. By properly modifying the modulation sequence, the method is aimed at reducing the amount of wasted power, analytically computed through a very effective closed-form relationship, while constraining the radiation pattern at the carrier frequency below a fixed sidelobe level. Representative results are reported and compared with previously published solutions to assess the effectiveness of the proposed approach.

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Paolo Rocca

Papers

Differential Evolution as Applied to Electromagnetics

Evolutionary optimization as applied to inverse scattering problems

Unconventional Phased Array Architectures and Design Methodologies—A Review

Compressive Sensing in Electromagnetics - A Review

Handling Sideband Radiations in Time-Modulated Arrays Through Particle Swarm Optimization