Abstract: The traditional NSGA-Ⅱalgorithm uses the crowding degree as the second index of elite selection, which is difficult to maintain the balance of population convergence and diversity when dealing with high-dimensional multi-objective optimization problems due to insufficient selection pressure and intensified optimization conflicts between different objectives. To solve these problems, an improved NSGA-Ⅱalgorithm based on external archive updating and truncation mechanism was proposed: NSGA-Ⅱ-UTEA. The algorithm firstly introduces the external archiving mechanism based on decision variable decomposition into elite selection, then updates the external archiving according to the sum of the weight vector and hyperplane distance between individuals, and realizes the truncation of external archiving based on the Angle calculation between individuals, which further improves the convergence and diversity of the algorithm in the high-dimensional multi-objective optimization problem. Compared with the five classical evolutionary algorithms, NSGA-Ⅱ, NSGA-Ⅲ, MOEA/D , NSGA-Ⅱ-ARSBX and RPD-NSGA-Ⅱ. The experimental results show that NSGA-Ⅱ-UTEA algorithm is superior to other algorithms in the performance indexes of high-dimensional DTLZ and WFG series test functions with more than 10 targets, and has significant advantages in the distribution and diversity of solution sets. In particular, the best performance index values can be obtained for most high-dimensional WFG4-7 concave problems. Compared with the traditional NSGA-Ⅱalgorithm, the IGD performance of NSGA-Ⅱ-UTEA algorithm is improved by 50.6% on average on the high-dimensional DTLZ series test functions with more than 10 targets. In the high-dimensional WFG series test functions with 15 targets and above, the HV performance is improved by 60.7% on average. Experimental results verify the effectiveness of the improved NSGA-Ⅱ-UTEA algorithm.