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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

An experimental setup, made up of a three-limbed ferromagnetic core fitted with pickup coils, has been designed and built to be used as test bench for validation of magnetic field analysis with hysteresis. Unidirectional or rotational flux patterns and distorted waveforms can be generated by modifying the supply conditions of the coils. A detailed description of the structure with its supply and pickup coils is given. The analysis has been developed under different operating conditions. The experimental results of local and integral quantities are presented and compared with finite-element methods simulations.Index Terms-Finite-element methods (FEMs), hysteresis, magnetic analysis, magnetic measurements.

The aim of this work is to propose and validate a novel multi-objective optimization algorithm based on the emulation of the behaviour of the immune system. The rationale of this work is that the artificial immune system has, in its elementary structure, the main features required by other multi-objective evolutionary algorithms described in the literature, such as diversity preservation, memory, adaptivity, and elitism. The proposed approach is compared with three multi-objective evolutionary algorithms that are representative of the state of the art in multi-objective optimization. Algorithms are tested on six standard problems (both unconstrained and constrained) and comparisons are carried out using three different metrics. Results show that the proposed approach has very good performances and can become a valid alternative to standard algorithms for solving multi-objective optimization problems.