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

A procedure to significantly reduce the computational cost of the simulated annealing optimization algorithm coupled with analysis methods is proposed for EM devices. The chosen approach relies on the application of the simulated annealing method to an analytical approximation of the true objective function, expressed in the form of a multiquadric expansion. The algorithm is fully described, its potential advantages are pointed out and some test cases showing the effectiveness of the implemented strategy are reported and discussed. As an example the superconducting magnet energy storage device is mentione

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