In the reference vector based high-dimensional many-objective evolutionary algorithms， the random selection of parent individuals will slow down the speed of convergence， and the lack of individuals assigned to some reference vectors will weaken the diversity of population. In order to solve these problems， an Improved high-dimensional Many-Objective Evolutionary Algorithm based on Decomposition （IMaOEA/D） was proposed. Firstly， when a reference vector was assigned at least two individuals in the framework of decomposition strategy， the parent individuals were selected for reproduction of offspring according to the distance from the individual assigned to the reference vector to the ideal point， so as to increase the search speed. Then， for the reference vector that was not assigned at least two individuals， the point with the smallest distance from the ideal point along the reference vector was selected from all the individuals， so that at least two individuals and the reference vector were associated. Meanwhile， by guaranteeing one individual was related to each reference vector after environmental selection， the diversity of population was ensured. The proposed method was tested and compared with other four high-dimensional many-objective optimization algorithms based on decomposition on the MaF test problem sets with 10 and 15 objectives. Experimental results show that， the proposed algorithm has good optimization ability for high-dimensional many-objective optimization problems： the optimization results of the proposed algorithm on 14 test problems of the 30 test problems are better than those of the other four comparison algorithms. Especially， the proposed algorithm has certain advantage on the degradation problem optimization.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020121895