An improved aquila optimization algorithm integrating stochastic opposition-based learning and a mutated teaching-learning-based optimization strategy.
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| Title: | An improved aquila optimization algorithm integrating stochastic opposition-based learning and a mutated teaching-learning-based optimization strategy. |
|---|---|
| Authors: | SONG, Yijia1, ZHANG, Xiaoqing1, SUN, Minmin1, ZHANG, Li1, LI, Na1, ZENG, Junzhe1 |
| Source: | Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. May2026, Vol. 48 Issue 5, p936-950. 15p. |
| Subjects: | Metaheuristic algorithms, Optimization algorithms, Multidisciplinary design optimization, Mathematical optimization |
| Abstract: | To address the shortcomings of slow convergence speed and susceptibility to local optima in the standard aquila optimization (AO) algorithm, an improved aquila optimization algorithm, TAO algorithm, is proposed by integrating stochastic opposition-based learning and a mutated teaching-learning-based optimization (TLBO) strategy. Firstly, an expanded search strategy is introduced in the initial phase to enhance the diversity of initial space exploration, and differential mutation is employed to improve the quality of optimization. Secondly, a stochastic opposition-based learning strategy is adopted to increase the number of elite individuals, thereby enhancing the algorithm's search quality. Furthermore, individual positions are updated through t-distribution mutation perturbations to boost the diversity of the search space. Meanwhile, the TLBO strategy is integrated, leveraging the teaching-learning synergy strategy to accelerate the algorithm's convergence speed. Then, simulation experiments were carried out on 23 functions with diverse characteristics (unimodal, multimodal, and fixed-dimension multimodal) selected from the CEC 2005 benchmark test suite. The results demonstrate that, compared to AO algorithm and several other heuristic intelligent optimization algorithms, TAO algorithm exhibits superior performance in terms of optimization accuracy, convergence, and stability. The Wilcoxon ranksum test results further verify that the search performance of TAO is significantly different from that of the comparative algorithms, and TAO outperforms the comparison algorithms. Finally, three engineering design optimization cases are introduced to further validate the feasibility of TAO algorithm in solving practical problems. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194237706 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An improved aquila optimization algorithm integrating stochastic opposition-based learning and a mutated teaching-learning-based optimization strategy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22SONG%2C+Yijia%22">SONG, Yijia</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22ZHANG%2C+Xiaoqing%22">ZHANG, Xiaoqing</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22SUN%2C+Minmin%22">SUN, Minmin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22ZHANG%2C+Li%22">ZHANG, Li</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22LI%2C+Na%22">LI, Na</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22ZENG%2C+Junzhe%22">ZENG, Junzhe</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Engineering+%26+Science+%2F+Jisuanji+Gongcheng+yu+Kexue%22">Computer Engineering & Science / Jisuanji Gongcheng yu Kexue</searchLink>. May2026, Vol. 48 Issue 5, p936-950. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Multidisciplinary+design+optimization%22">Multidisciplinary design optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To address the shortcomings of slow convergence speed and susceptibility to local optima in the standard aquila optimization (AO) algorithm, an improved aquila optimization algorithm, TAO algorithm, is proposed by integrating stochastic opposition-based learning and a mutated teaching-learning-based optimization (TLBO) strategy. Firstly, an expanded search strategy is introduced in the initial phase to enhance the diversity of initial space exploration, and differential mutation is employed to improve the quality of optimization. Secondly, a stochastic opposition-based learning strategy is adopted to increase the number of elite individuals, thereby enhancing the algorithm's search quality. Furthermore, individual positions are updated through t-distribution mutation perturbations to boost the diversity of the search space. Meanwhile, the TLBO strategy is integrated, leveraging the teaching-learning synergy strategy to accelerate the algorithm's convergence speed. Then, simulation experiments were carried out on 23 functions with diverse characteristics (unimodal, multimodal, and fixed-dimension multimodal) selected from the CEC 2005 benchmark test suite. The results demonstrate that, compared to AO algorithm and several other heuristic intelligent optimization algorithms, TAO algorithm exhibits superior performance in terms of optimization accuracy, convergence, and stability. The Wilcoxon ranksum test results further verify that the search performance of TAO is significantly different from that of the comparative algorithms, and TAO outperforms the comparison algorithms. Finally, three engineering design optimization cases are introduced to further validate the feasibility of TAO algorithm in solving practical problems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3969/j.issn.1007-130X.2026.05.017 Languages: – Code: chi Text: Chinese PhysicalDescription: Pagination: PageCount: 15 StartPage: 936 Subjects: – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Multidisciplinary design optimization Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: An improved aquila optimization algorithm integrating stochastic opposition-based learning and a mutated teaching-learning-based optimization strategy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: SONG, Yijia – PersonEntity: Name: NameFull: ZHANG, Xiaoqing – PersonEntity: Name: NameFull: SUN, Minmin – PersonEntity: Name: NameFull: ZHANG, Li – PersonEntity: Name: NameFull: LI, Na – PersonEntity: Name: NameFull: ZENG, Junzhe IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1007130X Numbering: – Type: volume Value: 48 – Type: issue Value: 5 Titles: – TitleFull: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue Type: main |
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