Very few work has been done to test multi-robot task allocation (MRTA)
swarm algorithms in real time systems, where each task must be executed before a
deadline. In this paper a comparative study has been done between several swarm
algorithms and a centralized task allocation method. Moreover, a new swarm algorithm is proposed which improves significantly the results using very few communication capacity between robots. This new algorithm can reduce the interference between robots produced when two or more robots select the same task to execute. A very simple but effective learning algorithm has been also implemented to fit the parameters of this algorithm. To verify the results a foraging task has been used under different environments. The results show that the performance of this algorithm is very close to some centralized approaches results.