用大模型自动调优无人机通信算法超参数，效果优于人工和随机方法。
LLM Agent for Hyper-Parameter Optimization
- 构建大模型代理，通过迭代调用WS-PSO-CM算法探索超参数空间。
- 生成的超参数使最小总速率显著高于人工和随机方法。
- 适合需要自动化调参的通信算法研发人员参考。
超参数对通信算法性能至关重要。然而，当前针对基于无线电图的无人机轨迹与通信优化所设计的温启粒子群优化带交叉变异（WS-PSO-CM）算法的超参数优化方法多为启发式，自动化程度低且性能可提升空间大。本文设计了一个大语言模型（LLM）代理用于超参数自动调优，采用迭代框架与模型上下文协议（MCP）。首先，通过配置文件定义超参数范围、任务目标、终止条件、保守或激进策略及模型参数；随后，LLM代理迭代调用WS-PSO-CM算法进行探索；最后根据终止条件退出循环并返回优化后的超参数集。实验表明，由本方法生成的超参数所实现的最小总速率显著高于人工启发式与随机生成方法，说明融合PSO与WS-PSO-CM知识的LLM代理在寻找高性能超参数方面具有实际价值。
原文摘要 · Abstract (English)
Hyper-parameters are essential and critical for the performance of communication algorithms. However, current hyper-parameters optimization approaches for Warm-Start Particles Swarm Optimization with Crossover and Mutation (WS-PSO-CM) algorithm, designed for radio map-enabled unmanned aerial vehicle (UAV) trajectory and communication, are primarily heuristic-based, exhibiting low levels of automation and improvable performance. In this paper, we design an Large Language Model (LLM) agent for automatic hyper-parameters-tuning, where an iterative framework and Model Context Protocol (MCP) are applied. In particular, the LLM agent is first set up via a profile, which specifies the boundary of hyper-parameters, task objective, terminal condition, conservative or aggressive strategy of optimizing hyper-parameters, and LLM configurations. Then, the LLM agent iteratively invokes WS-PSO-CM algorithm for exploration. Finally, the LLM agent exits the loop based on the terminal condition and returns an optimized set of hyperparameters. Our experiment results show that the minimal sum-rate achieved by hyper-parameters generated via our LLM agent is significantly higher than those by both human heuristics and random generation methods. This indicates that an LLM agent with PSO and WS-PSO-CM algorithm knowledge is useful in seeking high-performance hyper-parameters.
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