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

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The problem of design optimization is of high industrial interest, and has been extensively studied for years, with excellent results. However, there is the well‐known issue of a reasonable balance between the computational effort usually required by stochastic methods, and the fact that deterministic optimizers, even though much more efficient, are not guaranteed to localize a good minimum, as they can remain trapped in the first found local one. To overcome these problems, the authors developed a hybrid strategy, which gave good results in terms of speed and reliability of the obtained optima, especially when the objective function is obtained through a finite element analysis, due, for example, to the absence of an analytical solution of the problem, and the direct use of a stochastic method would be unfeasible for practical purposes, because of the intolerable processing time required. Copyright © 2001 John Wiley & Sons, Ltd.

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