HypaEvo: Capability-Aligned LLM Scoring for Metaheuristic Hyperparameter Control

Dynamic hyperparameter control is a long-standing challenge for population-based metaheuristics. Learning-based controllers achieve strong performance but require expensive training and generalize poorly. Recent zero-shot approaches use Large Language Models (LLMs) to bypass training, but existing architectures route the final control decision through open-ended numerical generation (a regime in which LLMs are comparatively less reliable) while leaving their stronger capabilities in ordinal assessment underexploited. We propose **HypaEvo**, a *capability-aligned dual-LLM scoring architecture* that confines the LLM to bounded ordinal judgment and delegates numerical actuation to deterministic code. Two specialized agents independently rate the urgency of exploration and exploitation on a discrete ordinal scale (0–10); a deterministic coordinator translates the score differential into proportional, range-normalized parameter adjustments via a fixed direction map, enforcing directional priors and parameter bounds by construction. This separates qualitative reasoning from numerical arithmetic, removing open-ended numerical generation from the actuation step. Across four combinatorial optimization problems (TSP, CVRP, FSSP, and UAV trajectory optimization) and three metaheuristic families (GA, PSO, and ACO) with Qwen3-30B as the backbone, our method consistently outperforms representative state-of-the-art learning-based, neural-evolution, and LLM-driven controllers.