Planar-mirror reflection imaging learning based seagull optimization algorithm for global optimization and feature selection
Seagull optimization algorithm (SOA) exhibits certain weaknesses such as poor accuracy and a tendency to stagnate in local optimal solutions when solving complex optimization problems. This paper suggests an enhanced variant of SOA, referred to as planar-mirror reflection imaging learning based SOA...
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| Veröffentlicht in: | Knowledge-based systems Jg. 317; S. 113420 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
23.05.2025
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| ISSN: | 0950-7051 |
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| Abstract | Seagull optimization algorithm (SOA) exhibits certain weaknesses such as poor accuracy and a tendency to stagnate in local optimal solutions when solving complex optimization problems. This paper suggests an enhanced variant of SOA, referred to as planar-mirror reflection imaging learning based SOA (PRIL-SOA), to address these limitations. First, we present the novel nonlinear strategies for adjusting the employing variable A and control parameter B are presented to achieve a balance between global and local search capabilities. Second, a modified position update equation is devised that incorporates velocity components and personal history best positions, thereby enhance solution precision. Third, a new PRIL strategy is introduced to maintain diversity and prevent premature convergence. To validate the performance of PRIL-SOA, we conduct a series of benchmark tests, including 23 classical functions and a feature selection problem involving 21 datasets are used. The results indicate that PRIL-SOA consistently outperforms basic SOA and other meta-heuristics. The average search success rate of PRIL-SOA on benchmark test problems is 91.3 %, with 21 out of 23 problems achieving the theoretical optimal value. Compare with SOA, mountain gazelle optimizer (MGO), whale optimization algorithm (WOA), hunger games search (HGS), HHO-based joint opposite selection (HHO-JOS), modified SCA (MSCA), and exploration-enhanced GWO (EEGWO), the average success rates of PRIL-SOA is better to 86.95 %, 78.26 %, 82.61 %, 65.22 %, 56.52 %, 60.87 %, and 4.35 %, respectively. |
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| AbstractList | Seagull optimization algorithm (SOA) exhibits certain weaknesses such as poor accuracy and a tendency to stagnate in local optimal solutions when solving complex optimization problems. This paper suggests an enhanced variant of SOA, referred to as planar-mirror reflection imaging learning based SOA (PRIL-SOA), to address these limitations. First, we present the novel nonlinear strategies for adjusting the employing variable A and control parameter B are presented to achieve a balance between global and local search capabilities. Second, a modified position update equation is devised that incorporates velocity components and personal history best positions, thereby enhance solution precision. Third, a new PRIL strategy is introduced to maintain diversity and prevent premature convergence. To validate the performance of PRIL-SOA, we conduct a series of benchmark tests, including 23 classical functions and a feature selection problem involving 21 datasets are used. The results indicate that PRIL-SOA consistently outperforms basic SOA and other meta-heuristics. The average search success rate of PRIL-SOA on benchmark test problems is 91.3 %, with 21 out of 23 problems achieving the theoretical optimal value. Compare with SOA, mountain gazelle optimizer (MGO), whale optimization algorithm (WOA), hunger games search (HGS), HHO-based joint opposite selection (HHO-JOS), modified SCA (MSCA), and exploration-enhanced GWO (EEGWO), the average success rates of PRIL-SOA is better to 86.95 %, 78.26 %, 82.61 %, 65.22 %, 56.52 %, 60.87 %, and 4.35 %, respectively. |
| ArticleNumber | 113420 |
| Author | Xu, Ming Tang, Mingzhu Wu, Tiebin Yang, Yang Jiao, Hui Long, Wen |
| Author_xml | – sequence: 1 givenname: Wen surname: Long fullname: Long, Wen organization: School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550025, PR China – sequence: 2 givenname: Hui surname: Jiao fullname: Jiao, Hui organization: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, PR China – sequence: 3 givenname: Yang surname: Yang fullname: Yang, Yang organization: School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550025, PR China – sequence: 4 givenname: Ming surname: Xu fullname: Xu, Ming organization: School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550025, PR China – sequence: 5 givenname: Mingzhu surname: Tang fullname: Tang, Mingzhu organization: School of Energy Power and Engineering, Changsha University of Science & Technology, Changsha 410114, PR China – sequence: 6 givenname: Tiebin surname: Wu fullname: Wu, Tiebin email: wutiebin81@163.com organization: School of Energy and Electrical Engineering, Hunan University of Humanities Science & Technology, Loudi 417000, PR China |
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| Keywords | Seagull optimization algorithm Feature selection Gaussian function Function optimization |
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| Title | Planar-mirror reflection imaging learning based seagull optimization algorithm for global optimization and feature selection |
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