To avoid the effort associated with choosing control parameter settings, an adaptive approach for parameter setting of a multi-objective Particle Swarm Optimization algorithm is presented in this work. The adaptive parameter control relies on methods from Design of Experiments which are able to detect significant performance variations of parameter settings. Furthermore, interaction effects of different parameters can be discovered. The adaptive control is applied to the parameters which are incorporated in the update equations of PSO, so the movement of particles is adapted based on feedback about successes during the search. The adaptive approach is evaluated using 13 test functions and several performance measures.