Hybrid PSO-WOA approach for an efficient task offloading in mobile edge computing.
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| Title: | Hybrid PSO-WOA approach for an efficient task offloading in mobile edge computing. |
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| Authors: | Cherhabil, Fatima Z.1 f.cherhabil@univ-batna2.dz, Bendib, Sonia-Sabrina1 ss.bendib@univ-batna2.dz, Sedrati, Maamar1 m.sedrati@univbatna2.dz, Adouane, Chahrazed1 adouane.c@gmail.com, Benflis, Sifeddine1 sif.benflis@univbatna2.dz |
| Source: | Telkomnika. Apr2026, Vol. 24 Issue 2, p514-526. 13p. |
| Subjects: | Particle swarm optimization, Software-defined networking, Internet of things, Resource allocation, Scheduling, Edge computing, Metaheuristic algorithms |
| Abstract: | Offering a promising solution for latency-sensitive and resource-constrained internet of things (IoT) applications, mobile edge computing (MEC) extends cloud capabilities to the network edge. However, the decentralized nature of edge resources, coupled with stringent latency requirements and IoT energy constraints, presents significant challenges for efficient task offloading. Integrating IoT with MEC and software-defined networking (SDN) can meet the growing demands for low latency and energy-aware resource management. This paper proposes a hybrid evolutionary algorithm combining whale optimization algorithm (WOA) and particle swarm optimization (PSO) with crossover, mutation, and Lévy flight operators (CML) to balance exploration and exploitation. The algorithm minimizes a weighted sum function (energy 35%, delay 35%, and monetary cost 30%) for joint task offloading and resource allocation in SDN-enabled MEC environments. The proposed approach is evaluated against six well-known metaheuristics, analyzing performance across various metrics including scalability with up to 100 users. Experimental results, validated by nonparametric statistical tests, demonstrate that the proposed algorithm achieves statistically significant improvements in convergence speed, solution quality, and scalability, making it a robust and promising candidate for real-time MEC task scheduling. [ABSTRACT FROM AUTHOR] |
| Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University 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: 194026206 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid PSO-WOA approach for an efficient task offloading in mobile edge computing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cherhabil%2C+Fatima+Z%2E%22">Cherhabil, Fatima Z.</searchLink><relatesTo>1</relatesTo><i> f.cherhabil@univ-batna2.dz</i><br /><searchLink fieldCode="AR" term="%22Bendib%2C+Sonia-Sabrina%22">Bendib, Sonia-Sabrina</searchLink><relatesTo>1</relatesTo><i> ss.bendib@univ-batna2.dz</i><br /><searchLink fieldCode="AR" term="%22Sedrati%2C+Maamar%22">Sedrati, Maamar</searchLink><relatesTo>1</relatesTo><i> m.sedrati@univbatna2.dz</i><br /><searchLink fieldCode="AR" term="%22Adouane%2C+Chahrazed%22">Adouane, Chahrazed</searchLink><relatesTo>1</relatesTo><i> adouane.c@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Benflis%2C+Sifeddine%22">Benflis, Sifeddine</searchLink><relatesTo>1</relatesTo><i> sif.benflis@univbatna2.dz</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Telkomnika%22">Telkomnika</searchLink>. Apr2026, Vol. 24 Issue 2, p514-526. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Software-defined+networking%22">Software-defined networking</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Offering a promising solution for latency-sensitive and resource-constrained internet of things (IoT) applications, mobile edge computing (MEC) extends cloud capabilities to the network edge. However, the decentralized nature of edge resources, coupled with stringent latency requirements and IoT energy constraints, presents significant challenges for efficient task offloading. Integrating IoT with MEC and software-defined networking (SDN) can meet the growing demands for low latency and energy-aware resource management. This paper proposes a hybrid evolutionary algorithm combining whale optimization algorithm (WOA) and particle swarm optimization (PSO) with crossover, mutation, and Lévy flight operators (CML) to balance exploration and exploitation. The algorithm minimizes a weighted sum function (energy 35%, delay 35%, and monetary cost 30%) for joint task offloading and resource allocation in SDN-enabled MEC environments. The proposed approach is evaluated against six well-known metaheuristics, analyzing performance across various metrics including scalability with up to 100 users. Experimental results, validated by nonparametric statistical tests, demonstrate that the proposed algorithm achieves statistically significant improvements in convergence speed, solution quality, and scalability, making it a robust and promising candidate for real-time MEC task scheduling. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University 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: 10.12928/TELKOMNIKA.v24i2.27293 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 514 Subjects: – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Software-defined networking Type: general – SubjectFull: Internet of things Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Scheduling Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Metaheuristic algorithms Type: general Titles: – TitleFull: Hybrid PSO-WOA approach for an efficient task offloading in mobile edge computing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cherhabil, Fatima Z. – PersonEntity: Name: NameFull: Bendib, Sonia-Sabrina – PersonEntity: Name: NameFull: Sedrati, Maamar – PersonEntity: Name: NameFull: Adouane, Chahrazed – PersonEntity: Name: NameFull: Benflis, Sifeddine IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16936930 Numbering: – Type: volume Value: 24 – Type: issue Value: 2 Titles: – TitleFull: Telkomnika Type: main |
| ResultId | 1 |
