Abstract: Much work has been done to implement metasearch engines with different rank aggregation methods. However, those methods do not have the ability to deal with the exploding data from huge amount of Web sources as well as the multiplying requirements of metasearch users. In this paper, we take the view that the rank aggregation problem can be solved with a multi-objective optimizer if the quality requirements of a user are considered along with the queries, and we find that the user’s preferences among those quality requirements can help reduce the solution space. Accordingly, we propose an evolutionary rank aggregation algorithm based on user preferences. We bring a new encoding scheme for MOPSO, leverage new definitions of position and velocity, modify initialization methods of the particle swarms, improve the turbulence operator, and adjust strategies of external archive updating and leader selection, aiming at building a discrete multi-objective optimizer based on decomposition and dominance (D\+3MOPSO) to map out the best aggregated ranking quickly and accurately from a large-scale discrete solution space. We have the proposed algorithm along with several state-of-the-art rank aggregation methods tested on 4 datasets of different sizes: the LETOR MQ2008-agg dataset, a Web dataset, a synthetically simulated dataset and an extended Web dataset. The experiment results demonstrate that our method outperforms machine-learning-based algorithms and other multi-objective evolutionary algorithms by convergence, performance and efficiency especially when dealing with the large-scale metasearch rank aggregation tasks.