Many-Objective optimization problem (MOP) with more than four objectives are among the most difficult problems in the field of evolutionary multi-objective optimization. In fact, existing multi-objective evolutionary algorithms (MOEAs) can not fulfill the engineering requirement of convergence, diversity and stability. In this paper, a new kind of many-objective evolutionary algorithm is proposed. The algorithm adopts a global ranking technique to favor convergence by improving selection pressure without need of the user's preference or objective information, avoiding loss of rationality and credibility due to the use of relaxed Pareto domination relations. In addition, a new global density estimation method based on the harmonic average distance is presented. Finally, a new elitist selection strategy is designed. Simulation results on DTLZ{1,2,4,5} test problems with 4~30 objectives show that the proposed algorithm consistently provides good convergence as the number of objectives increases, outperforming five state-of-the-art MOEAs.