An Enhanced Snow Geese Optimizer Integrating Multiple Strategies for Numerical Optimization
An enhanced snow geese algorithm (ESGA) is proposed to address the problems of the weakened population diversity and unbalanced search tendencies encountered by the snow geese algorithm (SGA) in the search process. First, an adaptive switching strategy is used to dynamically select the search strate...
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MDPI AG
2025-06-01
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| author | Baoqi Zhao Yu Fang Tianyi Chen |
| author_facet | Baoqi Zhao Yu Fang Tianyi Chen |
| author_sort | Baoqi Zhao |
| collection | DOAJ |
| description | An enhanced snow geese algorithm (ESGA) is proposed to address the problems of the weakened population diversity and unbalanced search tendencies encountered by the snow geese algorithm (SGA) in the search process. First, an adaptive switching strategy is used to dynamically select the search strategy to balance the exploitation and exploration capabilities. Second, a dominant group guidance strategy is introduced to improve the population quality. Finally, a dominant stochastic difference search strategy is designed to enrich the population diversity and help it escape from the local optimum by co-directing effects in multiple directions. Ablation experiments were performed on the CEC2017 test set to illustrate the improvement mechanism and the degree of compatibility of their improved strategies. The proposed ESGA with a highly cited algorithm and the powerful improved algorithm are compared on the CEC2022 test suite, and the experimental results confirm that the ESGA outperforms the compared algorithms. Finally, the ability of the ESGA to solve complex problems is further highlighted by solving the robot path planning problem. |
| format | Article |
| id | doaj-art-0073eb7211e14c5ebe72d8d2dbfeb789 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Biomimetics |
| spelling | doaj-art-0073eb7211e14c5ebe72d8d2dbfeb7892025-08-20T03:26:55ZengMDPI AGBiomimetics2313-76732025-06-0110638810.3390/biomimetics10060388An Enhanced Snow Geese Optimizer Integrating Multiple Strategies for Numerical OptimizationBaoqi Zhao0Yu Fang1Tianyi Chen2Institute of Artificial Intelligence Application, Ningbo Polytechnic, Ningbo 315800, ChinaTaizhou Institute of Zhejiang University, Taizhou 318000, ChinaSolar Energy Research Institute of Singapore, National University of Singapore, Singapore 117574, SingaporeAn enhanced snow geese algorithm (ESGA) is proposed to address the problems of the weakened population diversity and unbalanced search tendencies encountered by the snow geese algorithm (SGA) in the search process. First, an adaptive switching strategy is used to dynamically select the search strategy to balance the exploitation and exploration capabilities. Second, a dominant group guidance strategy is introduced to improve the population quality. Finally, a dominant stochastic difference search strategy is designed to enrich the population diversity and help it escape from the local optimum by co-directing effects in multiple directions. Ablation experiments were performed on the CEC2017 test set to illustrate the improvement mechanism and the degree of compatibility of their improved strategies. The proposed ESGA with a highly cited algorithm and the powerful improved algorithm are compared on the CEC2022 test suite, and the experimental results confirm that the ESGA outperforms the compared algorithms. Finally, the ability of the ESGA to solve complex problems is further highlighted by solving the robot path planning problem.https://www.mdpi.com/2313-7673/10/6/388snow geese algorithmmeta-heuristic algorithmadaptive switching strategydominant group guidancedominant stochastic difference searchCEC 2017 and CEC 2022 benchmark functions |
| spellingShingle | Baoqi Zhao Yu Fang Tianyi Chen An Enhanced Snow Geese Optimizer Integrating Multiple Strategies for Numerical Optimization Biomimetics snow geese algorithm meta-heuristic algorithm adaptive switching strategy dominant group guidance dominant stochastic difference search CEC 2017 and CEC 2022 benchmark functions |
| title | An Enhanced Snow Geese Optimizer Integrating Multiple Strategies for Numerical Optimization |
| title_full | An Enhanced Snow Geese Optimizer Integrating Multiple Strategies for Numerical Optimization |
| title_fullStr | An Enhanced Snow Geese Optimizer Integrating Multiple Strategies for Numerical Optimization |
| title_full_unstemmed | An Enhanced Snow Geese Optimizer Integrating Multiple Strategies for Numerical Optimization |
| title_short | An Enhanced Snow Geese Optimizer Integrating Multiple Strategies for Numerical Optimization |
| title_sort | enhanced snow geese optimizer integrating multiple strategies for numerical optimization |
| topic | snow geese algorithm meta-heuristic algorithm adaptive switching strategy dominant group guidance dominant stochastic difference search CEC 2017 and CEC 2022 benchmark functions |
| url | https://www.mdpi.com/2313-7673/10/6/388 |
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