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The analysis of preference rankings has become an important topic in the general field of data analysis in recent years. The classic meaning of preference rankings understood as orders expressed by a series of judges have been joined by the concept of judges is no longer always understood as human beings, but as resulting from automatic evaluation procedures. This paper provides a particle swarm-based optimization algorithm that provides an accurate solution to the rank aggregation problem, namely producing a ranking that best synthesizes the orderings stated by each judge, when the number of items to be evaluated is large