Very few studies have been carried out to test multi-robot task allocation swarm algorithms in real time systems, where each task must be executed before a deadline. This paper presents a comparative study of several swarm-like algorithms and auction based methods for this kind of scenarios. Moreover, a new paradigm called pseudo-probabilistic swarm-like, is proposed, which merges characteristics of deterministic and probabilistic classical swarm approaches. Despite that this new paradigm can not be classified as swarming, it is closely related with swarm methods. Pseudo-probabilistic swarm-like algorithms can reduce the interference between robots and are particularly suitable for real time environments. This work presents two pseudo-probabilistic swarm-like algorithms: distance pseudo-probabilistic and robot pseudoprobabilistic. The experimental results show that the pseudo-probabilistic swarm-like methods significantly improve the number of finished tasks before a deadline, compared to classical swarm algorithms. Furthermore, a very simple but effective learning algorithm has been implemented to fit the parameters of these new methods. To verify the results a foraging task has been used under different configurations.