Academic Journal
Effects of parameters of particle swarm optimization algorithms on the path planning for flexible needles.
| Τίτλος: | Effects of parameters of particle swarm optimization algorithms on the path planning for flexible needles. |
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| Συγγραφείς: | Zhang, Feifan, Yu, Longfeng, Hong, Songhe, Huang, Ye, Wang, Yu, Peng, Zhenwan |
| Πηγή: | PeerJ Computer Science; Feb2026, p1-20, 20p |
| Θεματικοί όροι: | Particle swarm optimization, Needles & pins, Genetic algorithms, Kinematics, Computer simulation, Mathematical optimization, Robotic path planning, Surgery |
| Περίληψη: | Background: Percutaneous puncture has wide applications in various clinical tasks. Path planning is crucial but challenging. Particle swarm optimization (PSO) algorithms are proper candidates to solve this problem and have been introduced. However, path planning errors exist and the effects of PSO parameters have not yet been studied. Methods: A kinematic model was developed using the D-H method. The best combination of PSO parameters for flexible needle-path planning was obtained by the genetic algorithm with the Lord strategy. According to simulation results, an iterative PSO is proposed. Results: Simulations demonstrate that the deviation was reduced to 0.017 mm on average (mostly close to zero) with optimized parameters. An accuracy of 0.001 mm can be guaranteed by proposed iterative PSO. Conclusions: The optimized parameters can significantly reduce the path deviation, and provide an optimal path with a simple PSO. The proposed iterative PSO can attain superior performance while consuming less computation time. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 192850758 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1041.06896972656 |
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| Items | – Name: Title Label: Title Group: Ti Data: Effects of parameters of particle swarm optimization algorithms on the path planning for flexible needles. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Feifan%22">Zhang, Feifan</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Longfeng%22">Yu, Longfeng</searchLink><br /><searchLink fieldCode="AR" term="%22Hong%2C+Songhe%22">Hong, Songhe</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Ye%22">Huang, Ye</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yu%22">Wang, Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Peng%2C+Zhenwan%22">Peng, Zhenwan</searchLink> – Name: TitleSource Label: Source Group: Src Data: PeerJ Computer Science; Feb2026, p1-20, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Needles+%26+pins%22">Needles & pins</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Kinematics%22">Kinematics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Surgery%22">Surgery</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Percutaneous puncture has wide applications in various clinical tasks. Path planning is crucial but challenging. Particle swarm optimization (PSO) algorithms are proper candidates to solve this problem and have been introduced. However, path planning errors exist and the effects of PSO parameters have not yet been studied. Methods: A kinematic model was developed using the D-H method. The best combination of PSO parameters for flexible needle-path planning was obtained by the genetic algorithm with the Lord strategy. According to simulation results, an iterative PSO is proposed. Results: Simulations demonstrate that the deviation was reduced to 0.017 mm on average (mostly close to zero) with optimized parameters. An accuracy of 0.001 mm can be guaranteed by proposed iterative PSO. Conclusions: The optimized parameters can significantly reduce the path deviation, and provide an optimal path with a simple PSO. The proposed iterative PSO can attain superior performance while consuming less computation time. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of PeerJ Computer Science is the property of PeerJ Inc. 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: 10.7717/peerj-cs.3569 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Needles & pins Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Kinematics Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Robotic path planning Type: general – SubjectFull: Surgery Type: general Titles: – TitleFull: Effects of parameters of particle swarm optimization algorithms on the path planning for flexible needles. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Feifan – PersonEntity: Name: NameFull: Yu, Longfeng – PersonEntity: Name: NameFull: Hong, Songhe – PersonEntity: Name: NameFull: Huang, Ye – PersonEntity: Name: NameFull: Wang, Yu – PersonEntity: Name: NameFull: Peng, Zhenwan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23765992 Titles: – TitleFull: PeerJ Computer Science Type: main |
| ResultId | 1 |