用大模型引导进化搜索，快速找到雷达多目标跟踪的最优功率分配方案。
Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution
- 通过大模型引导进化搜索，自动发现闭式解析解。
- 相比传统方法，精度损失仅1.51%，速度提升超千倍。
- 适合实时雷达调度、低数据依赖场景，可推广至工程优化问题。
高效雷达资源分配是基础但计算挑战巨大的问题，最优解通常需要复杂迭代优化。为满足实时调度、强泛化性和低数据依赖需求，本文提出一种新范式：利用大语言模型（LLM）引导的进化搜索（AlphaEvolve），自主发现多目标跟踪的闭式功率分配解。该方法将高维雷达状态编码为物理启发特征，进化出紧凑可解释的评分函数，并通过确定性约束满足变换生成可行功率分配。大量实验表明，所发现的闭式解在追踪精度上接近最优（平均相对性能损失仅1.51%），在不同场景和目标数量下具有可靠泛化能力，且相比传统迭代求解器提速超过三个数量级。这些结果表明，LLM引导的符号搜索有望革新雷达资源管理及更广泛的工程优化问题。
原文摘要 · Abstract (English)
Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimization with high complexity. Motivated by the need for real-time scheduling, robust generalization, and low data dependency, this paper proposes a novel paradigm that leverages large language model (LLM)-guided evolutionary search (AlphaEvolve) to autonomously discover a closed-form power allocation solution for multi-target tracking. The approach encodes high-dimensional radar states into physically inspired features, then evolves a compact and interpretable scoring function, which is transformed to feasible power allocations via a deterministic constraint-satisfying transformation. Extensive experiments demonstrate that the discovered closed-form solution achieves near-optimal tracking accuracy (average relative performance loss of only $1.51\%$), reliable generalization across diverse scenarios and target counts, and over three orders of magnitude speedup compared to conventional iterative solvers. These results highlight the potential of LLM-guided symbolic search to revolutionize not only radar resource management but also broader classes of engineering optimization problems.
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