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Component tolerances and parameter variations may considerably influence the performance of industrial sensors. Particularly for capacitive sensors, changes of environmental conditions can substantially influence the behavior of the front-end electronics. An effective approach to improve the quality of these products is worst case design. A novel algorithm based on particle swarm optimization is presented in this paper. Local models of the fitness function are used to speed up the search procedure. Results for both robust and nonrobust optimization of capacitive sensors utilizing a carrier frequency measurement principle are presented and compared. They show that robust optimization can provide significant improvements.