An LLM guided multi objective optimization framework for cluster 2027 Applie

The rapid growth of electric vehicles (EVs) is increasing stress on power distribution networks by amplifying peak demand and reducing charging-station utilization under uncoordinated charging. This study proposes a Large Language Model-guided Hyper-Heuristic (LLM-HH) for adaptive EV charging coordination. The framework uses a lightweight LLM-based meta-controller that monitors compact optimization feedback signals, including hypervolume (HV) improvement, population diversity, and stagnation, to orchestrate a portfolio of multi-objective evolutionary algorithms (MOEAs) through constrained solver selection and bounded parameter updates. Under an hourly coordination model, the framework coordinates a portfolio of MOEAs to jointly optimize three competing objectives: reducing on-peak demand, improving charger utilization, and preserving a cluster-level energy-per-session proxy across three behaviorally distinct charging regimes: Quick Top-Up, Home-Based, and Public-Stay. Experiments on a real-world Korean EV charging dataset demonstrate that the optimization-feedback-driven LLM-HH consistently improves Pareto front quality, achieving statistically significant improvements in Pareto-front quality.