Applied Sciences, Vol. 13, Pages 3589: Hybrid Strategy to Improve the High-Dimensional Multi-Target Sparrow Search Algorithm and Its Application

Applied Sciences, Vol. 13, Pages 3589: Hybrid Strategy to Improve the High-Dimensional Multi-Target Sparrow Search Algorithm and Its Application

Applied Sciences doi: 10.3390/app13063589

Authors: Lu Ren Wenyu Zhang Yunrui Ye Xinru Li

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.