Abstract
Genetic algorithms are becoming a common tool for optimal design applications, where, due to the multiple solutions issue, global search techniques are required. Anyway, when dealing with real problems, involving several degrees of freedom, the actual computing power restricts the global search ability.The availability of cheap hardware has recently caused the spreading of multiprocessors computing systems. In particular, new genetic techniques have been proposed to adapt the method's characteristics to the parallel architecture, allowing in this way also to deal with real problems. Dividing the population into subgroups, and letting each group to evolve on one of the processors, interacting only when scheduled, can implement one of these techniques, called niching approach.Objective of this work is to discuss the perspectives of niching approaches in the electromagnetic optimal design applications. As an example case, preliminary results about SMES (Superconducting Magnetic Energy Storage) devices are proposed.