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This paper investigates the idea of having multiple swarms working separately and cooperating with each other to solve an optimization problem. Many factors that influence the behavior of this approach haven't been properly studied. This paper investigates two factors that affect this approach behavior. These factors are: (i) the communication strategy adopted if the number of swarms is raised above two, and (ii) the number of cooperating swarms. Experiments run on different benchmark optimization functions show that adopting a circular communication strategy gives better results than just sharing the global best of all the swarms. Increasing the number of cooperating swarms provides better results provided that the appropriate synchronization period is selected.