The conflict between convergence and distribution of population will intensify when the dimensionality of objective increases. The selection operator of the traditional multi-objective evolutionary algorithm is difficult to balance the convergence and distribution of the population. To solve this problem, a multi-objective evolutionary algorithm based on adaptive aggregation distance(MOEA-AAD) is proposed. Firstly, the reference point dominance relation is used to replace the original Pareto dominance relation to increase the selection pressure and strengthen the convergence. Then, an adaptive aggregation distance is proposed to adaptively adjust the ratio of convergence and distribution through dynamically changing penalty parameters. Finally, a method with a deletion operator is designed to improve the niche selection strategy, and the selection and elimination operation are carried out according to the value of the adaptive aggregation distance. In order to prove the optimization performance of the proposed algorithm, it is compared with four other excellent multi-objective evolutionary algorithms on the test problem and applied to two real-world applications. The simulation results show that the proposed algorithm has better comprehensive performance, which can effectively balance the convergence and distribution of the population.