Application of Pied Kingfisher Optimizer with Hybrid Strategy Improvement in Path Planning and Engineering Design.
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| Title: | Application of Pied Kingfisher Optimizer with Hybrid Strategy Improvement in Path Planning and Engineering Design. |
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| Authors: | Xiong, Xiangnan1 1206017946@qq.com, Yu, Peng2 oykunpeng@163.com, Chen, Xuebo3 xuebochen@126.com |
| Source: | Engineering Letters. Feb2026, Vol. 34 Issue 2, p664-685. 22p. |
| Subjects: | Robotic path planning, Engineering design, Optimization algorithms, Differential evolution, Metaheuristic algorithms |
| Abstract: | The metaheuristic optimization algorithm finds broad applications in fields such as machine learning, engineering design, and control. The Pied Kingfisher Optimizer, emerging in recent years, is an efficient metaheuristic algorithm that draws inspiration from the distinctive hunting strategies and symbiotic interactions of the pied kingfisher observed in their natural environment. However, it still grapples with sluggish convergence and a tendency to become trapped in local optima when tackling specific complex problems. To counter these drawbacks, this study develops a modified Pied Kingfisher Optimizer. This improvement integrates three hybrid mechanisms: the Firefly Algorithm, Differential Evolution, and an adaptive t-distribution mutation operator. Ablation testing via the CEC2017 and CEC2022 suites evaluated the operational capability of the proposed algorithm. Furthermore, the upgraded algorithm was rigorously benchmarked against several other metaheuristic optimizers. Empirical findings indicate that the refined Pied Kingfisher Optimizer achieves a higher overall performance level compared to the chosen competitor algorithms. This performance superiority was statistically confirmed using the Friedman test. Additionally, to validate the practical utility of the Pied Kingfisher Optimizer with hybrid strategy improvement in control and engineering domains, experiments were conducted on robot path planning and engineering design problems, where it demonstrated commendable performance. These results further confirm the superiority of the improved PKO algorithm in the fields of control systems and engineering design. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191342748 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of Pied Kingfisher Optimizer with Hybrid Strategy Improvement in Path Planning and Engineering Design. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiong%2C+Xiangnan%22">Xiong, Xiangnan</searchLink><relatesTo>1</relatesTo><i> 1206017946@qq.com</i><br /><searchLink fieldCode="AR" term="%22Yu%2C+Peng%22">Yu, Peng</searchLink><relatesTo>2</relatesTo><i> oykunpeng@163.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xuebo%22">Chen, Xuebo</searchLink><relatesTo>3</relatesTo><i> xuebochen@126.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Feb2026, Vol. 34 Issue 2, p664-685. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+design%22">Engineering design</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The metaheuristic optimization algorithm finds broad applications in fields such as machine learning, engineering design, and control. The Pied Kingfisher Optimizer, emerging in recent years, is an efficient metaheuristic algorithm that draws inspiration from the distinctive hunting strategies and symbiotic interactions of the pied kingfisher observed in their natural environment. However, it still grapples with sluggish convergence and a tendency to become trapped in local optima when tackling specific complex problems. To counter these drawbacks, this study develops a modified Pied Kingfisher Optimizer. This improvement integrates three hybrid mechanisms: the Firefly Algorithm, Differential Evolution, and an adaptive t-distribution mutation operator. Ablation testing via the CEC2017 and CEC2022 suites evaluated the operational capability of the proposed algorithm. Furthermore, the upgraded algorithm was rigorously benchmarked against several other metaheuristic optimizers. Empirical findings indicate that the refined Pied Kingfisher Optimizer achieves a higher overall performance level compared to the chosen competitor algorithms. This performance superiority was statistically confirmed using the Friedman test. Additionally, to validate the practical utility of the Pied Kingfisher Optimizer with hybrid strategy improvement in control and engineering domains, experiments were conducted on robot path planning and engineering design problems, where it demonstrated commendable performance. These results further confirm the superiority of the improved PKO algorithm in the fields of control systems and engineering design. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters 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: 22 StartPage: 664 Subjects: – SubjectFull: Robotic path planning Type: general – SubjectFull: Engineering design Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Differential evolution Type: general – SubjectFull: Metaheuristic algorithms Type: general Titles: – TitleFull: Application of Pied Kingfisher Optimizer with Hybrid Strategy Improvement in Path Planning and Engineering Design. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiong, Xiangnan – PersonEntity: Name: NameFull: Yu, Peng – PersonEntity: Name: NameFull: Chen, Xuebo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 2 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
