Improved snow geese algorithm for engineering applications and clustering optimization
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 performanc...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88080-7 |
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| _version_ | 1823862498769502208 |
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| author | Haihong Bian Can Li Yuhan Liu Yuxuan Tong Shengwei Bing Jincheng Chen Quance Ren Zhiyuan Zhang |
| author_facet | Haihong Bian Can Li Yuhan Liu Yuxuan Tong Shengwei Bing Jincheng Chen Quance Ren Zhiyuan Zhang |
| author_sort | Haihong Bian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-936a27ead1f84b52b65dee4aa0d257f0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-936a27ead1f84b52b65dee4aa0d257f02025-02-09T12:30:19ZengNature PortfolioScientific Reports2045-23222025-02-0115119510.1038/s41598-025-88080-7Improved snow geese algorithm for engineering applications and clustering optimizationHaihong Bian0Can Li1Yuhan Liu2Yuxuan Tong3Shengwei Bing4Jincheng Chen5Quance Ren6Zhiyuan Zhang7College of Electrical Engineering, Nanjing Institute of TechnologyCollege of Electrical Engineering, Nanjing Institute of TechnologyCollege of Electrical Engineering, Nanjing Institute of TechnologyCixi Power Supply Company of State Grid Zhejiang Electric Power Co., LtdCollege of Electrical Engineering, Nanjing Institute of TechnologyCollege of Electrical Engineering, Nanjing Institute of TechnologyCollege of Electrical Engineering, Nanjing Institute of TechnologyCollege of Electrical Engineering, Nanjing Institute of TechnologyAbstract 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.https://doi.org/10.1038/s41598-025-88080-7Snow geese algorithmMeta-heuristic algorithmEngineering and clustering optimizationLead goose rotation mechanismHonk-guiding mechanismOutlier boundary |
| spellingShingle | Haihong Bian Can Li Yuhan Liu Yuxuan Tong Shengwei Bing Jincheng Chen Quance Ren Zhiyuan Zhang Improved snow geese algorithm for engineering applications and clustering optimization Scientific Reports Snow geese algorithm Meta-heuristic algorithm Engineering and clustering optimization Lead goose rotation mechanism Honk-guiding mechanism Outlier boundary |
| title | Improved snow geese algorithm for engineering applications and clustering optimization |
| title_full | Improved snow geese algorithm for engineering applications and clustering optimization |
| title_fullStr | Improved snow geese algorithm for engineering applications and clustering optimization |
| title_full_unstemmed | Improved snow geese algorithm for engineering applications and clustering optimization |
| title_short | Improved snow geese algorithm for engineering applications and clustering optimization |
| title_sort | improved snow geese algorithm for engineering applications and clustering optimization |
| topic | Snow geese algorithm Meta-heuristic algorithm Engineering and clustering optimization Lead goose rotation mechanism Honk-guiding mechanism Outlier boundary |
| url | https://doi.org/10.1038/s41598-025-88080-7 |
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