An improved arithmetic optimization algorithm with hybrid elite pool strategies
This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and...
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| Published in: | Soft computing (Berlin, Germany) Vol. 28; no. 2; pp. 1127 - 1155 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.01.2024
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| Subjects: | |
| ISSN: | 1432-7643, 1433-7479 |
| Online Access: | Get full text |
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| Abstract | This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. |
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| AbstractList | This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. |
| Author | Zhang, Xingong Chen, Zhaohui Zhang, Hanxiao Cao, Zhong Liu, Haiyang |
| Author_xml | – sequence: 1 givenname: Haiyang surname: Liu fullname: Liu, Haiyang organization: School of Intelligent Technology and Engineering, Chongqing University of Science and Technology – sequence: 2 givenname: Xingong surname: Zhang fullname: Zhang, Xingong organization: School of Mathematics Science, Chongqing Normal University – sequence: 3 givenname: Hanxiao surname: Zhang fullname: Zhang, Hanxiao organization: School of Intelligent Technology and Engineering, Chongqing University of Science and Technology – sequence: 4 givenname: Zhong surname: Cao fullname: Cao, Zhong organization: School of Electronics and Communication Engineering, Guangzhou University – sequence: 5 givenname: Zhaohui orcidid: 0000-0001-8870-9564 surname: Chen fullname: Chen, Zhaohui email: z.chen@cqust.edu.cn organization: School of Mathematics, Physics and Data Science, Chongqing University of Science and Technology |
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| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Arithmetic optimization algorithm (AOA) Mathematical optimization acceleration (MOA) function Hybrid elite pool strategy Meta-heuristic algorithms |
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| SubjectTerms | Artificial Intelligence Computational Intelligence Control Engineering Mathematical Logic and Foundations Mechatronics Optimization Robotics |
| Title | An improved arithmetic optimization algorithm with hybrid elite pool strategies |
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