A multi-strategy improved snake optimizer and its application to SVM parameter selection
Support vector machine (SVM) is an effective classification tool and maturely used in various fields. However, its performance is very sensitive to parameters. As a newly proposed swarm intelligence algorithm, snake optimizer algorithm (SO) can help to solve the parameter selection problem. Neverthe...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-10-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024322 |
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| author | Hong Lu Hongxiang Zhan Tinghua Wang |
| author_facet | Hong Lu Hongxiang Zhan Tinghua Wang |
| author_sort | Hong Lu |
| collection | DOAJ |
| description | Support vector machine (SVM) is an effective classification tool and maturely used in various fields. However, its performance is very sensitive to parameters. As a newly proposed swarm intelligence algorithm, snake optimizer algorithm (SO) can help to solve the parameter selection problem. Nevertheless, SO has the shortcomings of weak population initialization, slow convergence speed in the early stage, and being easy to fall into local optimization. To address these problems, an improved snake optimizer algorithm (ISO) was proposed. The mirror opposition-based learning mechanism (MOBL) improved the population quality to enhance the optimization speed. The novel evolutionary population dynamics model (NEPD) was beneficial for searching accurately. The differential evolution strategy (DES) helped to reduce the probability of falling into local optimal value. The experimental results of classical benchmark functions and CEC2022 showed that ISO had higher optimization precision and faster convergence rate. In addition, it was also applied to the parameter selection of SVM to demonstrate the effectiveness of the proposed ISO. |
| format | Article |
| id | doaj-art-a9eb75719a7f414a88b6674d300c471b |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-a9eb75719a7f414a88b6674d300c471b2025-01-23T07:48:00ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-10-0121107297733610.3934/mbe.2024322A multi-strategy improved snake optimizer and its application to SVM parameter selectionHong Lu0Hongxiang Zhan1Tinghua Wang2School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, ChinaSchool of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, ChinaSchool of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, ChinaSupport vector machine (SVM) is an effective classification tool and maturely used in various fields. However, its performance is very sensitive to parameters. As a newly proposed swarm intelligence algorithm, snake optimizer algorithm (SO) can help to solve the parameter selection problem. Nevertheless, SO has the shortcomings of weak population initialization, slow convergence speed in the early stage, and being easy to fall into local optimization. To address these problems, an improved snake optimizer algorithm (ISO) was proposed. The mirror opposition-based learning mechanism (MOBL) improved the population quality to enhance the optimization speed. The novel evolutionary population dynamics model (NEPD) was beneficial for searching accurately. The differential evolution strategy (DES) helped to reduce the probability of falling into local optimal value. The experimental results of classical benchmark functions and CEC2022 showed that ISO had higher optimization precision and faster convergence rate. In addition, it was also applied to the parameter selection of SVM to demonstrate the effectiveness of the proposed ISO.https://www.aimspress.com/article/doi/10.3934/mbe.2024322snake optimizersupport vector machine (svm)parameter optimizationopposition-based learning |
| spellingShingle | Hong Lu Hongxiang Zhan Tinghua Wang A multi-strategy improved snake optimizer and its application to SVM parameter selection Mathematical Biosciences and Engineering snake optimizer support vector machine (svm) parameter optimization opposition-based learning |
| title | A multi-strategy improved snake optimizer and its application to SVM parameter selection |
| title_full | A multi-strategy improved snake optimizer and its application to SVM parameter selection |
| title_fullStr | A multi-strategy improved snake optimizer and its application to SVM parameter selection |
| title_full_unstemmed | A multi-strategy improved snake optimizer and its application to SVM parameter selection |
| title_short | A multi-strategy improved snake optimizer and its application to SVM parameter selection |
| title_sort | multi strategy improved snake optimizer and its application to svm parameter selection |
| topic | snake optimizer support vector machine (svm) parameter optimization opposition-based learning |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024322 |
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