Abstract. In this thesis, the application of Particle Swarm Optimization in groups of autonomous robots to solve optimization problems in real world search spaces is investigated both for single-objective and multi-objective optimization problems. In order to reduce the power consumption of the robots and to allow cooperation of all particles even if they are in different search space regions, a stationary communication infrastructure is used. The resulting, indirect social interaction of the particles is analysed and the applicability of the new algorithm is shown on several single-objective and multi-objective benchmark functions.