A Large Language Model-based Multi-Agent Framework to Autonomously Design Algorithms for Earth Observation Satellite Scheduling Problem

The Earth observation satellite scheduling problem (EOSSP) is a complex optimization problem involving the selection, sequencing, and timing of observational tasks to maximize task completion rates while adhering to various constraints. However, effectively solving diverse scheduling scenarios with...

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Vydané v:	Engineering (Beijing, China)
Hlavní autori:	Chen, Jiawei, Chen, Yingguo, Pham, Duc Trong, Song, Yanjie, Wu, Jian, Xing, Lining, Chen, Yingwu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.11.2025
Predmet:	Automatic algorithm design Earth Observation Satellite Scheduling Large language models Multi-Agent Framework Earth Observation Satellite Scheduling Multi-Agent Framework Large language models Automatic algorithm design
ISSN:	2095-8099
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Popis
Shrnutí:	The Earth observation satellite scheduling problem (EOSSP) is a complex optimization problem involving the selection, sequencing, and timing of observational tasks to maximize task completion rates while adhering to various constraints. However, effectively solving diverse scheduling scenarios with varying characteristics through manual design and modification of algorithm configurations is a laborious task. Concurrently, with the advent of large language models (LLMs), numerous studies have leveraged related technologies to facilitate automatic algorithm deisgn (AAD), thereby alleviating the burdensome task of crafting optimization algorithms. However, these studies primarily concentrated on traditional optimization problems, whereas the EOSSP constitutes a more intricate problem necessitating precise articulation in specialized language and comprehensive elucidation. Moreover, previous research has achieved AAD by depending on the initially defined algorithms and proposing sophisticated, detailed iterative frameworks, thereby still requiring expertise in algorithm design. In this paper, we introduce an LLM-based multi-agent framework AgentAD that automatically generates efficient algorithms for EOSSP—transforming problem descriptions in natural language into executable algorithmic code. Within this framework, the proposed agents collaborate through numerous atomic interactions, completing subtasks in each defined workflow phase. Mirroring human behavior in software development processes, these agents join forces to craft effective algorithms tailored to EOSSP scenarios. The experimental results demonstrate that the algorithms generated by AgentAD outperformed other state-of-the-art human-designed counterparts.
ISSN:	2095-8099
DOI:	10.1016/j.eng.2025.10.027