###### Abstract  
This research combines the improved reference point selection strategy and the sparrow search algorithm with an enhanced competition mechanism to create a high-dimensional multi-objective sparrow search algorithm with an incorporated improved reference point selection strategy. First, the reference point selection approach is utilized to establish the reference points and sparrow populations, and the most important reference points are dynamically chosen to increase the global search ability. Then, the size of the search population and the method of searcher position updating are dynamically adjusted according to the size of the entropy difference between two adjacent generations of the population. Following, the convergence speed is increased by improving the follower position formula and extending the competition mechanism to high-dimensional multi-objective optimization. The Corsi variation operator improves the algorithm's capacity to break out of its local optimum. Finally, we have used 12 standard benchmark test functions to evaluate the MaOISSA (Many/Multi-Objective Sparrow Search Algorithm based on Improved reference points) and compared it with many high-dimensional multi-objective algorithms. There were nine with substantial IGD values and eight with significant HV values. The findings revealed that MaOISSA had convergence and variety. The simulated results of the performance model for the defense science and technology innovation ecosystem demonstrate that MaOISSA offers a superior solution for tackling the high-dimensional, multi-objective issue, demonstrating the method's efficacy. reference point selection strategy; multi/many-objective optimization; sparrow search algorithm; defense science and technology innovation ecosystem  
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search algorithm: literature [7; 8; 9; 10; 11] et al. increase the initial population variety using elite learning, random wandering, backward learning, enhanced Logistics chaos, or Tent chaos sequences. To increase the merit-seeking capacity of individual sparrows by adding an adaptive cross-variance operator, adaptive inertia weights, adaptive t-distribution strategy, and reverse learning strategy based on the lens principle, the discoverer population in the literature [11; 12; 13; 14; 15] was enhanced. Tactics such as t-distribution perturbation, differential variation for perturbation or variable spiral search strategy, were used to boost the convergence speed of the algorithm and its capacity to escape the local algorithm. Most of these improvements were performed for single-objective search optimization and in multi-objective optimization problems. Literature [16] et al. performed a multi-objective sparrow search through a novel congestion distance calculation strategy, and external archiving; literature [17] et al. performed multi-objective optimization by balancing global and local optima through a competitive mechanism for population selection. These enhanced algorithms make extensive use of the external archiving strategy in the storage of multi-dimensional solutions and perform well on multi-objective optimization problems. However, in high-dimensional multi-objective optimization problems with more than three objectives, the convergence speed decreases significantly with increasing dimensionality, and it is challenging to approximate the true Pareto frontier. A multi-objective optimization problem is a problem in which many sub-objectives within a system reach the optimal solution under certain conditions. Most of the current economic and social activities as well as practical engineering applications are multi-objective problems, and the high-dimensional multi-objective optimization problem is an extension of the Multi-objective Optimization Problem (MOP). We refer to problems with more than three objects as Multi/Many-Objective Optimization Problems (MaOP) [18].