Improved Fox Optimization Algorithm Based on Opposition-Based Learning Mechanism and Control Variable for Solving Engineering Optimization Problems.
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| Title: | Improved Fox Optimization Algorithm Based on Opposition-Based Learning Mechanism and Control Variable for Solving Engineering Optimization Problems. |
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| Authors: | Wang, Dongsheng1 wds0824@qq.com, Gao, Chuang2 13500422153@163.com, Gao, Biao3 gaobiao0910@qq.com |
| Source: | IAENG International Journal of Computer Science. Jun2026, Vol. 53 Issue 6, p2316-2327. 12p. |
| Subjects: | Metaheuristic algorithms, Global optimization, Parameterization, Engineering design, Multidisciplinary design optimization |
| Abstract: | The Fox Optimizer (FOX) is a meta-heuristic algorithm inspired by red foxes' snow-hunting behavior, simulating their "random prey searching" and "precise jumping for hunting" processes. To enhance FOX's global search capability and convergence performance, this paper proposes an improved algorithm (OBL4FOX) integrating the opposition-based learning (OBL) mechanism and optimized control variables. First, the OBL mechanism is introduced to generate opposite solutions, expanding the search space and strengthening global exploration. Second, the core control variable in FOX is improved via four non-linear dynamic update strategies, enhancing adaptability to complex solution spaces. Validated on the CEC2022 test function set, OBL4FOX exhibits superior performance. OBL4FOX is further applied to three types of typical engineering optimization problems: minimizing the manufacturing cost of pressure vessel design, minimizing the structural weight of three-bar truss design, and minimizing the comprehensive cost of welded beam design. The experimental results demonstrate that under the premise of satisfying all engineering constraints, OBL4FOX can obtain better objective function values when solving engineering problems, which verifies its effectiveness and practicality in actual complex engineering optimization scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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: 194196015 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Improved Fox Optimization Algorithm Based on Opposition-Based Learning Mechanism and Control Variable for Solving Engineering Optimization Problems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Dongsheng%22">Wang, Dongsheng</searchLink><relatesTo>1</relatesTo><i> wds0824@qq.com</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Chuang%22">Gao, Chuang</searchLink><relatesTo>2</relatesTo><i> 13500422153@163.com</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Biao%22">Gao, Biao</searchLink><relatesTo>3</relatesTo><i> gaobiao0910@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jun2026, Vol. 53 Issue 6, p2316-2327. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Global+optimization%22">Global optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Parameterization%22">Parameterization</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+design%22">Engineering design</searchLink><br /><searchLink fieldCode="DE" term="%22Multidisciplinary+design+optimization%22">Multidisciplinary design optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Fox Optimizer (FOX) is a meta-heuristic algorithm inspired by red foxes' snow-hunting behavior, simulating their "random prey searching" and "precise jumping for hunting" processes. To enhance FOX's global search capability and convergence performance, this paper proposes an improved algorithm (OBL4FOX) integrating the opposition-based learning (OBL) mechanism and optimized control variables. First, the OBL mechanism is introduced to generate opposite solutions, expanding the search space and strengthening global exploration. Second, the core control variable in FOX is improved via four non-linear dynamic update strategies, enhancing adaptability to complex solution spaces. Validated on the CEC2022 test function set, OBL4FOX exhibits superior performance. OBL4FOX is further applied to three types of typical engineering optimization problems: minimizing the manufacturing cost of pressure vessel design, minimizing the structural weight of three-bar truss design, and minimizing the comprehensive cost of welded beam design. The experimental results demonstrate that under the premise of satisfying all engineering constraints, OBL4FOX can obtain better objective function values when solving engineering problems, which verifies its effectiveness and practicality in actual complex engineering optimization scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 2316 Subjects: – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Global optimization Type: general – SubjectFull: Parameterization Type: general – SubjectFull: Engineering design Type: general – SubjectFull: Multidisciplinary design optimization Type: general Titles: – TitleFull: Improved Fox Optimization Algorithm Based on Opposition-Based Learning Mechanism and Control Variable for Solving Engineering Optimization Problems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Dongsheng – PersonEntity: Name: NameFull: Gao, Chuang – PersonEntity: Name: NameFull: Gao, Biao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 6 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
| ResultId | 1 |
