Some advantages of stochastic methods in multicriteria optimization of multibody systems

Optimization methods became an important tool for the synthesis of complex mechanical systems. However, while deterministic approaches usually yield good convergence within a few iterations, they often lead to local minima only. Stochastic optimization, like simulated annealing, is not as sensitive with respect to local minima, but often requires high computational effort. In this paper, we describe an optimization concept founded on either a deterministic gradient-based method or a stochastic simulated annealing optimization procedure. An application to vehicle dynamics and a comparison of the different procedures is given. It turns out that simulated annealing does not only avoid local minima, but also helps to give a dearer picture of the Edgeworth-Pareto optimal set and improves the judgement of the influence of nonsensitive parameters. Beside efficiency, these properties also have to be considered when it is decided whether a stochastic or a deterministic optimization algorithm should be chosen.