LLM Multi-Agent Tool Automates Satellite Scheduling Algorithm Design with Microsoft Power Automate Capabilities

A new multi-agent system, AgentAD, has been developed to automate the design of algorithms for Earth Observation Satellite Scheduling Problem (EOSSP). This complex task requires matching satellite observation windows, task priorities, onboard power and storage limits, and satellite attitude transition time constraints to maximize completed observation tasks. The traditional workflow involves aerospace and optimization experts manually building, testing, and revising standalone algorithms for every unique satellite-target scenario, a process marked by heavy time and resource costs.

The research team constructed AgentAD as a collaborative multi-agent system with five specialized roles: Algorithm Designer (AD), Algorithm Programmer (AP), Algorithm Optimizer (AO), Code Tester (CT), and Project Manager (PM). Each agent operates under tailored prompting paradigms that define fixed roles, task boundaries, input-output standards, and cross-agent communication protocols. The overall workflow mirrors real-world software development split into four sequential phases: design, optimization, testing, and documentation.

The system converts unstructured natural language descriptions of EOSSP scenarios into functional Python code through repeated atomic dialogue cycles between paired agents. Three core supporting mechanisms underpin stable collaboration: role specialization that restricts each agent to narrow professional duties, a permanent memory stream that archives all historical dialogue to avoid repeated errors, and chain-of-coding that breaks code writing and debugging into sequential subtasks to mitigate LLM hallucinations.

The AD first interprets raw scenario input and drafts initial algorithm frameworks including greedy, dynamic programming, and genetic structures. The PM coordinates agent communication and passes preliminary algorithm outlines to the AO, which proposes structural and parametric improvements to raise task completion performance. The AP translates revised algorithm logic into runnable annotated code, which the CT validates on EOSSP datasets and reports bugs or performance feedback back to the AO for iterative refinement.

This optimization-test loop repeats until convergence or predefined iteration limits, after which the PM compiles all dialogue records and algorithm iteration trails into standardized documentation. Ablation experiments confirm that the AO serves as the most indispensable component for performance improvement, followed by the CT for accurate feedback provision, while the memory stream delivers greater performance gains than the chain-of-coding module.

The team ran comparative trials across basic EOSSP settings with static constraints and complex scenarios augmented by dynamic task priorities, satellite forbidden time windows, and minimum imaging quality thresholds. The results show that algorithms autonomously evolved by AgentAD achieve higher task completion rates across most test environments, though the performance margin narrows as satellite and task scales expand due to increased contextual comprehension burdens on LLMs.

Convergence tracking shows AgentAD’s fitness metric Fi(a) rises steadily through iterative modification after identifying viable optimization trajectories. Trials across different large language models (LLMs), including GPT-3.5-Turbo, GPT-4o, DeepSeekV3 and Llama3.3, reveal more capable LLMs cut total atomic dialogue counts and token consumption without substantial shifts in final code length.

The authors outline plans to integrate multimodal satellite cloud data, introduce human expert feedback loops, and build visual interactive dashboards to enhance real-world operability of the automated algorithm generation pipeline. The research demonstrates the potential for large language models as a judge or decision-maker in complex optimization tasks, leveraging their capabilities to streamline data analysis tools and automate routine tasks.