Improved snow geese algorithm for engineering applications and clustering optimization

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...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 4506 - 95
Hlavní autoři: Bian, Haihong, Li, Can, Liu, Yuhan, Tong, Yuxuan, Bing, Shengwei, Chen, Jincheng, Ren, Quance, Zhang, Zhiyuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 06.02.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/705/1041
639/705/1042
Algorithms
Aquatic birds
Chen caerulescens
Convergence
Engineering and clustering optimization
Honk-guiding mechanism
Humanities and Social Sciences
Lead goose rotation mechanism
Meta-heuristic algorithm
multidisciplinary
Optimization
Outlier boundary
Science
Science (multidisciplinary)
Snow
Snow geese algorithm
Waterfowl
Snow geese algorithm
Meta-heuristic algorithm
Honk-guiding mechanism
Engineering and clustering optimization
Lead goose rotation mechanism
Outlier boundary
ISSN:2045-2322, 2045-2322
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Shrnutí: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-88080-7