An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm
This paper proposes an improved feature selection method based on an improved Slime Mould Algorithm (SMA), called the Triangular Mutation Rule Restart Strategy Slime Mould Algorithm (TRSMA), to overcome some of the shortcomings of the SMA, including premature convergence, poor population diversity,...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525001021 |
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| _version_ | 1849434427165245440 |
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| author | Ibrahim Musa Conteh Gibril Njai Abass Conteh Qingguo Du |
| author_facet | Ibrahim Musa Conteh Gibril Njai Abass Conteh Qingguo Du |
| author_sort | Ibrahim Musa Conteh |
| collection | DOAJ |
| description | This paper proposes an improved feature selection method based on an improved Slime Mould Algorithm (SMA), called the Triangular Mutation Rule Restart Strategy Slime Mould Algorithm (TRSMA), to overcome some of the shortcomings of the SMA, including premature convergence, poor population diversity, and local optima entrapment. The TRSMA uses three main strategies: (1) Good Point Set (GPS) initialization for better initial population diversity, (2) Triangular Mutation Rule (TMR) for better global exploration and finding higher-quality areas in the solution space, and (3) a Restart Strategy (RS) to reinitialize weak individuals to escape from local optimum. Then we combine the TRSMA with Support Vector Machines (SVM) and propose the TRSMA-SVM model to select the joint feature and classifier parameters. Experimental results on nine University of California, Irvine (UCI) datasets and a real-world malaria dataset show that TRSMA-SVM consistently outperforms recent state-of-the-art methods regarding classification accuracy with fewer selected features. Comparison with benchmark testing on CEC2017 functions confirms TRSMA’s ability to perform global optimization. Statistical tests using the Wilcoxon rank-sum and Friedman tests also verify these performance gains. The results illustrate that TRSMA is powerful and can handle complex high-dimensional optimization problems. |
| format | Article |
| id | doaj-art-941f133d011a4e399e45a1c4b37ddc9b |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-941f133d011a4e399e45a1c4b37ddc9b2025-08-20T03:26:39ZengElsevierEgyptian Informatics Journal1110-86652025-06-013010070910.1016/j.eij.2025.100709An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithmIbrahim Musa Conteh0Gibril Njai1Abass Conteh2Qingguo Du3School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; Department of Computer Science, Ernest Bai Koroma University of Science and Technology, Magburaka, Sierra LeoneDepartment of Computer Science, Ernest Bai Koroma University of Science and Technology, Magburaka, Sierra LeoneDepartment of Information Technology, Institute of Public Administration and Management, Freetown, Sierra LeoneSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; Corresponding author.This paper proposes an improved feature selection method based on an improved Slime Mould Algorithm (SMA), called the Triangular Mutation Rule Restart Strategy Slime Mould Algorithm (TRSMA), to overcome some of the shortcomings of the SMA, including premature convergence, poor population diversity, and local optima entrapment. The TRSMA uses three main strategies: (1) Good Point Set (GPS) initialization for better initial population diversity, (2) Triangular Mutation Rule (TMR) for better global exploration and finding higher-quality areas in the solution space, and (3) a Restart Strategy (RS) to reinitialize weak individuals to escape from local optimum. Then we combine the TRSMA with Support Vector Machines (SVM) and propose the TRSMA-SVM model to select the joint feature and classifier parameters. Experimental results on nine University of California, Irvine (UCI) datasets and a real-world malaria dataset show that TRSMA-SVM consistently outperforms recent state-of-the-art methods regarding classification accuracy with fewer selected features. Comparison with benchmark testing on CEC2017 functions confirms TRSMA’s ability to perform global optimization. Statistical tests using the Wilcoxon rank-sum and Friedman tests also verify these performance gains. The results illustrate that TRSMA is powerful and can handle complex high-dimensional optimization problems.http://www.sciencedirect.com/science/article/pii/S1110866525001021Good point setTriangular mutation ruleRestart strategySlime mould algorithmSupport vector machineFeature selection |
| spellingShingle | Ibrahim Musa Conteh Gibril Njai Abass Conteh Qingguo Du An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm Egyptian Informatics Journal Good point set Triangular mutation rule Restart strategy Slime mould algorithm Support vector machine Feature selection |
| title | An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm |
| title_full | An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm |
| title_fullStr | An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm |
| title_full_unstemmed | An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm |
| title_short | An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm |
| title_sort | optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm |
| topic | Good point set Triangular mutation rule Restart strategy Slime mould algorithm Support vector machine Feature selection |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525001021 |
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