<p>Many-objective evolutionary algorithms have demonstrated their superiority in dealing with many-objective optimization problems. However, their performance in handling many objective optimization problems can be significantly affected due to the sensitivity on the curvature of Pareto front. This paper proposes the dynamic mating selection strategy and strengthened fitness selection mechanism to solve many objective optimization problems (MaOEA-DMSF). In MaOEA-DMSF, a dynamic mating selection strategy is proposed to select appropriate mating population, which can generate high-quality offspring with a higher probability. In addition, a strengthened fitness selection mechanism is proposed to improve convergence without deterioration in population diversity. To verify the effectiveness of the proposed MaOEA-DMSF, a series of experiments are carried out against eight state-of-the-art many-objective optimization algorithms on three widely used benchmark test suites. Experimental results demonstrate that the proposed MaOEA-DMSF has higher competitiveness compared with peer competitors.</p>