The development of technologies for the additive manufacturing, in particular of metallic materials, is offering the possibility of producing parts with complex geometries. This opens up to the possibility of using topological optimization methods for the design of electromagnetic devices. Hence, a wide variety of approaches, originally developed for solid mechanics, have recently become attractive also in the field of electromagnetics. The general distinction between gradient-based and gradient-free methods drives the structure of the paper, with the latter becoming particularly attractive in the last years due to the concepts of artificial neural networks. The aim of this paper is twofold. On one hand, the paper aims at summarizing and describing the state-of-art on topology optimization techniques while on the other it aims at showing how the latter methodologies developed in non-electromagnetic framework (e.g., solid mechanics field) can be applied for the optimization of electromagnetic devices. Discussions and comparisons are both supported by theoretical aspects and numerical results.

Geared devices are commonly used to match the operating speed and torque of the power source with the second-mover. Such geared devices are usually mechanical gears. As counterparts of the conventional mechanical gears, magnetic gears are becoming promising devices, mainly due to the merits of physical isolation between moving parts, no gear lubrication, no mechanical fatigue, inherent overload protection and reduced maintenance, etc. Recently, several new topologies have been proposed to achieve better performances. In this paper, a general review of magnetic gears is presented, including a discussion of the most common and upcoming topologies and a description of the working principle. A comparison of different topologies is carried out, in terms of gear ratio and torque density. The main computational techniques generally adopted for coaxial magnetic gears are listed and described. Some key aspects concerning the design, the existed challenges and potential applications are also discussed. This paper aims to provide a comprehensive overview of the magnetic gear for readers working in this field.

This paper gives an overview of some stochastic optimization strategies, namely, evolution strategies, genetic algorithms, and simulated annealing, and how these methods can be applied to problems in electrical engineering. Since these methods usually require a careful tuning of the parameters which control the behavior of the strategies (strategy parameters), significant features of the algorithms implemented by the authors are presented. An analytical comparison among them is performed. Finally, results are discussed on three optimization problems.

Optimization metaheuristics, such as Particle Swarm Optimization, Ant Colony Optimization and bacterial foraging strategies have become very popular in the optimization community and have been successfully applied to electromagnetic device design. The Artificial Bee Colony (ABC) algorithm is a rather new bio-inspired swarm intelligence approach which is competitive with other population-based algorithms and has the advantage of using fewer control parameters. In this work, a standard and an improved version of the ABC algorithm using Gaussian distribution are applied to Loney's solenoid problem, showing the suitability of these methods for electromagnetic optimization.