###### Abstract
In contrast to single-objective optimization problems, the answer to a high-dimensional multi-objective optimization issue is typically a collection of numerous non-dominated alternatives known as the Pareto optimum solution set [19]. Therefore, solving high-dimensional multi-objective optimization problems requires finding as many non-dominated solutions with good convergence and diversity as possible. However, for high-dimensional complex problems, the higher the dimensionality, the pressure on the non-dominated solutions increases dramatically for traditional optimization algorithms, and the convergence speed decreases dramatically. Therefore, determining how to make the problem optimal and each element reasonably allocated has been a hot topic for the high-dimensional multi-objective optimization problem in recent years [20]. Combinatorial optimization challenges based on high-dimensional multi-objective and population intelligence are efficient solutions for high-dimensional multi-objective optimization issues that have evolved in recent years. A set of reference points are used to evaluate the quality of the solution, which assists in managing the population distribution in the objective space. As an extension of NSGA-II, NSGA-III is able to tackle high-dimensional multi-objective optimization problems more effectively and efficiently. The literature [21] maintains the diversity of multi-objective particle swarm algorithms by incorporating a reference point regeneration strategy; researchers [22] and others perform a global optimal selection strategy by counting the number of people connected to the centerline. Literature [23] evaluated the individual merit and directed the population crossover operation based on the algebra of algorithm runs. Literature [24] obtained the most efficient solution by finding the closest vector of people in the population and the reference point for ranking the individuals. However, the positions of reference points in these algorithms are pre-given, i.e., the parameters are set by themselves. The shape of the Pareto front of the optimization problem is generally unknown. Thus, the predetermined reference point placements may degrade the performance of the search algorithm. In this article, the reference space-based reference point selection technique suggested in the literature [20] adapts the total population size N to the number of reference points. The set of parameters in the search process is reduced, and then the correlation of reference points are used to judge the iteration stage the population is in and accelerate the population convergence. Considering the foregoing issues, this article combines the improved reference point selection strategy with the sparrow algorithm to solve the problem of convergence degradation that occurs when solving high-dimensional multi-objective optimization problems and improving the performance of the algorithm. First, the initial population and the reference points matching its number in the high-dimensional multi-objective problem are obtained by the reference point selection strategy in the case of an unknown Pareto frontier to reduce the human judgment. We used the entropy values of two successive generations of populations to assess the evolutionary phase of populations: in the nascent stage of population development, the entropy values of the two generations are similar. The complete set of reference points is used for environment selection, when the population is in the \"exploration\" stage and needs to expand the search range; in the late stage of population evolution, the filtered reference points are used for environment selection, when the population needs to accelerate convergence and enter the \"exploration\" stage.