A parallel implementation of a parametric optimization environment-numerical optimization of an inductor for traction drive systems

Optimum design is defined as a design that is the best possible solution. All design variables are determined simultaneously to satisfy a set of constraints and optimize a set of objectives. Two parametric FE pre-processors and a general purpose optimization environment are presented. Due to its open architecture, finite element as well as analytical models can be implemented. Stochastic algorithms usually require substantially more function evaluations compared to gradient methods, which increases the elapsed computation time. However, the stochastic algorithms feature unmatched simplicity in the setup of an optimization model. A parallel implementation of the evolution strategy is presented, which offers one way to reduce the elapsed computation time. An optimization task is discussed to outline the general application range of the developed tools. The optimum design of an inductor used in a traction drive system is described in detail. Special attention is paid to the formulation of the quality function.