A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems
For shortcomings of poor exploaration and parameter complexities of the butterfly optimization algorithm, an improved butterfly optimization algorithm based the self-adaption method (SABOA) was proposed to extremely enhance the searching accuracy and the iteration capability. SABOA has advantages of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9089033/ |
| _version_ | 1818330536322531328 |
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| author | Yuqi Fan Junpeng Shao Guitao Sun Xuan Shao |
| author_facet | Yuqi Fan Junpeng Shao Guitao Sun Xuan Shao |
| author_sort | Yuqi Fan |
| collection | DOAJ |
| description | For shortcomings of poor exploaration and parameter complexities of the butterfly optimization algorithm, an improved butterfly optimization algorithm based the self-adaption method (SABOA) was proposed to extremely enhance the searching accuracy and the iteration capability. SABOA has advantages of having fewer parameters, the simple algorithm structure, and the strong precision. First, a new fragrance coefficient was added to the basic butterfly optimization algorithm. Then, new iteration and updating strategies were introduced in global searching and local searching phases. Finally, this paper tested different optimization problems including low-high functions and constrained problems, and the obtained results were compared with other well-known algorithms, this paper also drew various mathematical statistics figures to comprehensively analyze searching performances of the proposed algorithms. The experimental results show that SABOA can get less number of function evaluations compared to other considered algorithms, which illustrates that SABOA has great searching balance, large exploration, and high iterative speed. |
| first_indexed | 2024-12-13T13:05:31Z |
| format | Article |
| id | doaj.art-62afcd1f78614d4da473e9611742189c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:05:31Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-62afcd1f78614d4da473e9611742189c2022-12-21T23:44:51ZengIEEEIEEE Access2169-35362020-01-018880268804110.1109/ACCESS.2020.29931489089033A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization ProblemsYuqi Fan0https://orcid.org/0000-0002-9914-7381Junpeng Shao1Guitao Sun2Xuan Shao3Key Laboratory of Advanced Manufacturing and Intelligent Technology, School of Mechanical and Power Engineering, Ministry of Education, Harbin University of Science and Technology, Harbin, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, School of Mechanical and Power Engineering, Ministry of Education, Harbin University of Science and Technology, Harbin, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, School of Mechanical and Power Engineering, Ministry of Education, Harbin University of Science and Technology, Harbin, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, School of Mechanical and Power Engineering, Ministry of Education, Harbin University of Science and Technology, Harbin, ChinaFor shortcomings of poor exploaration and parameter complexities of the butterfly optimization algorithm, an improved butterfly optimization algorithm based the self-adaption method (SABOA) was proposed to extremely enhance the searching accuracy and the iteration capability. SABOA has advantages of having fewer parameters, the simple algorithm structure, and the strong precision. First, a new fragrance coefficient was added to the basic butterfly optimization algorithm. Then, new iteration and updating strategies were introduced in global searching and local searching phases. Finally, this paper tested different optimization problems including low-high functions and constrained problems, and the obtained results were compared with other well-known algorithms, this paper also drew various mathematical statistics figures to comprehensively analyze searching performances of the proposed algorithms. The experimental results show that SABOA can get less number of function evaluations compared to other considered algorithms, which illustrates that SABOA has great searching balance, large exploration, and high iterative speed.https://ieeexplore.ieee.org/document/9089033/Butterfly optimization algorithmglobal optimizationconstrained problem |
| spellingShingle | Yuqi Fan Junpeng Shao Guitao Sun Xuan Shao A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems IEEE Access Butterfly optimization algorithm global optimization constrained problem |
| title | A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems |
| title_full | A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems |
| title_fullStr | A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems |
| title_full_unstemmed | A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems |
| title_short | A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems |
| title_sort | self adaption butterfly optimization algorithm for numerical optimization problems |
| topic | Butterfly optimization algorithm global optimization constrained problem |
| url | https://ieeexplore.ieee.org/document/9089033/ |
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