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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.
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]
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Database: Engineering Source
Description
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]
ISSN:02181266
DOI:10.1142/S0218126626501094