A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain.
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| Title: | A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain. |
|---|---|
| Authors: | Hamami, Mohd Ghazali Mohd1,2 (AUTHOR), Ismail, Zool Hilmi1,3 (AUTHOR) zool@utm.my |
| Source: | Archives of Computational Methods in Engineering. Apr2026, Vol. 33 Issue 3, p3083-3102. 20p. |
| Subjects: | Particle swarm optimization, Target acquisition, Optimization algorithms, Mathematical optimization, Swarm intelligence, Aggregation (Robotics) |
| Abstract: | Swarm Intelligence (SI) is one of the research fields that has continuously attracted researcher attention in these last two decades. The flexibility and a well-known decentralized collective behavior of its algorithm make SI a suitable candidate to be implemented in the swarm robotics domain for real-world optimization problems such as target search tasks. Since the introduction of Particle Swarm Optimization (PSO) as a representation of the SI algorithm, it has been widely accepted and utilized especially in local and global search strategies. Because of its simplicity, effectiveness, and low computational cost, PSO has retained popularity notably in the swarm robotics domain, and many improvements have been proposed. Target search problems are one of the areas that have been continuously solved by PSO. This article set out to analyze and give the inside view of the existing literature on PSO strategies towards target search problems. Based on the procedure of PRISMA Statement review method, a systematic review identified 51 related research studies. After further analysis of these total 51 selected articles and consideration on the PSO components, target search components, and research field components, resulting in nine main elements related to the discussed topic. The elements are PSO variant, application field, PSO inertial weight function, PSO efficiency improvement, PSO termination criteria, target available, target mobility status, experiment framework, and environment complexity. Several recommendations, opinions, and perfectives on the discussed topic are presented. Finally, recommendations for future research in this domain are represented to support future developments. [ABSTRACT FROM AUTHOR] |
| Copyright of Archives of Computational Methods in Engineering is the property of Springer Nature 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: 192983560 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hamami%2C+Mohd+Ghazali+Mohd%22">Hamami, Mohd Ghazali Mohd</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ismail%2C+Zool+Hilmi%22">Ismail, Zool Hilmi</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> zool@utm.my</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Archives+of+Computational+Methods+in+Engineering%22">Archives of Computational Methods in Engineering</searchLink>. Apr2026, Vol. 33 Issue 3, p3083-3102. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Target+acquisition%22">Target acquisition</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Aggregation+%28Robotics%29%22">Aggregation (Robotics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Swarm Intelligence (SI) is one of the research fields that has continuously attracted researcher attention in these last two decades. The flexibility and a well-known decentralized collective behavior of its algorithm make SI a suitable candidate to be implemented in the swarm robotics domain for real-world optimization problems such as target search tasks. Since the introduction of Particle Swarm Optimization (PSO) as a representation of the SI algorithm, it has been widely accepted and utilized especially in local and global search strategies. Because of its simplicity, effectiveness, and low computational cost, PSO has retained popularity notably in the swarm robotics domain, and many improvements have been proposed. Target search problems are one of the areas that have been continuously solved by PSO. This article set out to analyze and give the inside view of the existing literature on PSO strategies towards target search problems. Based on the procedure of PRISMA Statement review method, a systematic review identified 51 related research studies. After further analysis of these total 51 selected articles and consideration on the PSO components, target search components, and research field components, resulting in nine main elements related to the discussed topic. The elements are PSO variant, application field, PSO inertial weight function, PSO efficiency improvement, PSO termination criteria, target available, target mobility status, experiment framework, and environment complexity. Several recommendations, opinions, and perfectives on the discussed topic are presented. Finally, recommendations for future research in this domain are represented to support future developments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Archives of Computational Methods in Engineering is the property of Springer Nature 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: 10.1007/s11831-022-09819-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 3083 Subjects: – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Target acquisition Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Swarm intelligence Type: general – SubjectFull: Aggregation (Robotics) Type: general Titles: – TitleFull: A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hamami, Mohd Ghazali Mohd – PersonEntity: Name: NameFull: Ismail, Zool Hilmi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11343060 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Archives of Computational Methods in Engineering Type: main |
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