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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Published in:	Scientific reports Vol. 15; no. 1; pp. 4506 - 95
Main Authors:	Bian, Haihong, Li, Can, Liu, Yuhan, Tong, Yuxuan, Bing, Shengwei, Chen, Jincheng, Ren, Quance, Zhang, Zhiyuan
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 06.02.2025 Nature Publishing Group Nature Portfolio
Subjects:	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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Abstract	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.
AbstractList	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. Abstract 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. 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.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.
ArticleNumber	4506
Author	Tong, Yuxuan Chen, Jincheng Zhang, Zhiyuan Bing, Shengwei Ren, Quance Bian, Haihong Li, Can Liu, Yuhan
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Snippet	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... Abstract 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...
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SubjectTerms	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
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Title	Improved snow geese algorithm for engineering applications and clustering optimization
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