An improved multi-objective particle swarm optimization algorithm --- MOPSO-II. This paper proposes an improved multi-objective particle swarm optimization algorithm (MOPSO-II for short). In this algorithm, a disturbance vector is added to each particle to enhance the ability of escaping from local optima. Secondly, a property of limited time is assigned to each global best particle to keep effective sustainable search. Finally, an improved boundary treatment method is proposed to preserve the excellent search direction. Some experiments on a set of classical benchmark functions are conducted in the paper. Experimental results show that MOPSO-II using ε-dominance mechanism performs better than those using crowding distance method of MOPSO-II and NSGA2 on the distribution of solutions. It also shows that the proposed algorithm has better convergence than NSGA2. Consequently, MOPSO-II has some advantages in the field of solving multi-objective optimization problems.