The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

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The main goal of the Artap project is to provide an extensive infrastructure for robust design optimization, where usually many different numerical solvers have to be used together and the impact of the manufacturing uncertainties have to be minimized. Artap is an open-source software platform, developed jointly with the coupled numerical field solver, Agros-suite. Artap ensures interfaces for a broad collection of optimization algorithms (genetic and evolutionary algorithms, various interfaces to libraries such as Nlopt, Bayesopt, etc .), tools for machine learning (neural networks, Gaussian processes, etc. ), finite element solvers (Agros-suite, Comsol, Multiphysics, Deal.II). The implemented tools offers an easy and straightforward solution not only for robust design optimization but parameter identification, model order reduction, and shape optimization, as well. Moreover, Artap provides automatic parallelization of the optimization process. The paper presents the structure of the framework and technologies powering the project. The main features of Artap are demonstrated on an induction brazing process design tasks.

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