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Improved snow geese algorithm for engineering applications and clustering optimization
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DOI：10.1038/s41598-025-88080-7.png)
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Abstract
En 中文
The Snow Goose Algorithm (SGA) is a new meta-heuristic algorithm proposed in 2024, which has been proved to have good optimization effect, but there are still problems that are easy to fall into local optimal and premature convergence. In order to further improve the optimization performance of the algorithm, this paper proposes an improved Snow Goose algorithm (ISGA) based on three strategies according to the real migration habits of snow geese: (1) Lead goose rotation mechanism. (2) Honk-guiding mechanism. (3) Outlier boundary strategy. Through the above strategies, the exploration and development ability of the original algorithm is comprehensively enhanced, and the convergence accuracy and convergence speed are improved. In this paper, two standard test sets of IEEE CEC2022 and IEEE CEC2017 are used to verify the excellent performance of the improved algorithm. The practical application ability of ISGA is tested through 8 engineering problems, and ISGA is employed to enhance the effect of the clustering algorithm. The results show that compared with the comparison algorithm, the proposed ISGA has a faster iteration speed and can find better solutions, which shows its great potential in solving practical optimization problems.
Keywords:
Snow geese algorithm
Meta-heuristic algorithm
Engineering and clustering optimization
Lead goose rotation mechanism
Honk-guiding mechanism
Outlier boundary
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