Objective Although multi-objective particle swarm optimization is easy to implement and has a fast convergence speed it still needs to be further improved in the aspect of balancing convergence and diversity. Methods To solve the above problem a multi-objective particle swarm optimization with elite competition and comprehensive control ECMOPSO was proposed. On one hand the algorithm selected the elite particle set with global detriment and then the pairwise competitions were introduced into the multi-objective particle swarm optimization. The winner particles were selected through elite competition and the winner particles were fused with the global leaders to form more comprehensive social information so as to enhance the information interaction between particles in the population better guide the flight of particles in the population and improve the global exploration ability of the algorithm. On the other hand global detriment and shifted-based density estimation were combined to maintain the external archive so as to improve the quality of non-dominated solutions in the external archive and balance the convergence and diversity of the algorithm. Results The ECMOPSO algorithm four multi-objective particle swarm optimization algorithms and four multi-objective evolutionary algorithms were simulated for ZDT and UF series benchmark problems and the Wilcoxon rank sum test and the Friedman rank test were used to compare the overall performance of ECMOPSO algorithm with the selected comparison algorithms. The experimental results showed that compared with other comparison algorithms the convergence ability solution distribution and stability of the ECMOPSO algorithm have been improved to some extent. Conclusion ECMOPSO algorithm can balance convergence and diversity well improve its overall performance and can effectively solve most multi-objective optimization problems.