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For improving the diversity of existing multi-objective particle swarm optimization algorithm and keeping the balance between exploration and exploitation well, a multi-objective cellular PSO was proposed. The algorithm combined the concept of cellular automata with the multi-objective PSO theory. In addition, the relationship between the particles and the information transmission mechanism was studied, and a particle flight speed control strategy was presented. The results indicate that the improved algorithm outperforms the four compared algorithms concerning the convergence and diversity in solving multi-objective optimization problems with unconstraint and constraint. And also, the new algorithm can get more accurate solutions when applied in disc brake design problem.