A Mushroom Evolutionary Optimization Scheduling Algorithm Suitable for DAG Tasks in Satellite Cluster System.
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| Title: | A Mushroom Evolutionary Optimization Scheduling Algorithm Suitable for DAG Tasks in Satellite Cluster System. |
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| Authors: | Dan, Feng1,2 (AUTHOR) fengdan_bice@163.com, Shenwen, Wang2 (AUTHOR) wsw1244216554@163.com |
| Source: | Journal of Circuits, Systems & Computers. 8/30/2026, Vol. 35 Issue 14, p1-27. 27p. |
| Subjects: | Scheduling, Artificial satellites, Directed acyclic graphs, Evolutionary algorithms |
| Abstract: | The rapid development of satellite constellations has intensified the demand for efficient on-board mission scheduling mechanisms capable of operating within resource-constrained environments. In response, this study introduces the Mushroom Evolutionary Optimization Scheduling Algorithm (MEOSA), a novel approach designed for the execution of Directed Acyclic Graph (DAG) tasks in satellite cluster systems. Inspired by the survival strategies of mushrooms — specifically, the distributed information exchange within mycelium networks, cooperative — competitive environmental dynamics and adaptive propagation behaviors — MEOSA incorporates bio-inspired principles into scheduling design. The framework begins with the construction of a dynamic characterization model for satellite-based computing resources, accompanied by a spatio-temporal constraint model tailored to DAG task structures. Leveraging these biological insights, the algorithm establishes a distributed decision-making architecture and an evolutionary exploration mechanism to enhance environmental adaptability. Simulation results across multiple representative satellite mission scenarios demonstrate that MEOSA achieves a favorable balance between scheduling efficiency and resource utilization. Compared to conventional genetic and evolutionary algorithms, MEOSA exhibits faster convergence, lower computational overhead and improved suitability for real-time mission coordination in constrained environments. Ultimately, this work contributes a robust, bio-inspired theoretical framework that strengthens the autonomous scheduling capabilities of satellite clusters. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194008198 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Mushroom Evolutionary Optimization Scheduling Algorithm Suitable for DAG Tasks in Satellite Cluster System. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dan%2C+Feng%22">Dan, Feng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> fengdan_bice@163.com</i><br /><searchLink fieldCode="AR" term="%22Shenwen%2C+Wang%22">Shenwen, Wang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> wsw1244216554@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Circuits%2C+Systems+%26+Computers%22">Journal of Circuits, Systems & Computers</searchLink>. 8/30/2026, Vol. 35 Issue 14, p1-27. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellites%22">Artificial satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Directed+acyclic+graphs%22">Directed acyclic graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The rapid development of satellite constellations has intensified the demand for efficient on-board mission scheduling mechanisms capable of operating within resource-constrained environments. In response, this study introduces the Mushroom Evolutionary Optimization Scheduling Algorithm (MEOSA), a novel approach designed for the execution of Directed Acyclic Graph (DAG) tasks in satellite cluster systems. Inspired by the survival strategies of mushrooms — specifically, the distributed information exchange within mycelium networks, cooperative — competitive environmental dynamics and adaptive propagation behaviors — MEOSA incorporates bio-inspired principles into scheduling design. The framework begins with the construction of a dynamic characterization model for satellite-based computing resources, accompanied by a spatio-temporal constraint model tailored to DAG task structures. Leveraging these biological insights, the algorithm establishes a distributed decision-making architecture and an evolutionary exploration mechanism to enhance environmental adaptability. Simulation results across multiple representative satellite mission scenarios demonstrate that MEOSA achieves a favorable balance between scheduling efficiency and resource utilization. Compared to conventional genetic and evolutionary algorithms, MEOSA exhibits faster convergence, lower computational overhead and improved suitability for real-time mission coordination in constrained environments. Ultimately, this work contributes a robust, bio-inspired theoretical framework that strengthens the autonomous scheduling capabilities of satellite clusters. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company 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: 10.1142/S0218126626501094 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1 Subjects: – SubjectFull: Scheduling Type: general – SubjectFull: Artificial satellites Type: general – SubjectFull: Directed acyclic graphs Type: general – SubjectFull: Evolutionary algorithms Type: general Titles: – TitleFull: A Mushroom Evolutionary Optimization Scheduling Algorithm Suitable for DAG Tasks in Satellite Cluster System. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dan, Feng – PersonEntity: Name: NameFull: Shenwen, Wang IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 08 Text: 8/30/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02181266 Numbering: – Type: volume Value: 35 – Type: issue Value: 14 Titles: – TitleFull: Journal of Circuits, Systems & Computers Type: main |
| ResultId | 1 |
