To improve the convergence performance in dealing with multi-objective optimization problems(MOPs), a multiobjective evolutionary algorithm(MOEA) based on improved K-dominated sorting(KS-MODE) is proposed. KS-MODE improves ??-dominance to avoid the circular dominance and to enhance the selection pressure. The new global density estimation method, the elitist selection strategy and fitness evaluation function are also designed. In addition, the CAO operator is adopted to accelerate convergence. Simulation results on MOPs with 4-30 objectives show that, KS-MODE significantly outperforms several state-of-the-art MOEAs in terms of convergence, distribution and stability.