  In this paper, gradient-based optimization methods are combined with
finite-element modeling for improving electric devices. Geometric design
parameters are considered by affine decomposition of the geometry or by the
design element approach, both of which avoid remeshing. Furthermore, it is
shown how to robustify the optimization procedure, i.e., how to deal with
uncertainties on the design parameters. The overall procedure is illustrated by
an academic example and by the example of a permanent-magnet synchronous
machine. The examples show the advantages of deterministic optimization
compared to standard and popular stochastic optimization procedures such as,
e.g., particle swarm optimization.
