SPO: Discovering Adaptive Large Neighborhood Search Operators via Stackelberg Program Optimization

The paper introduces Stackelberg Program Optimization (SPO), a framework that uses large language models to discover adaptive destroy‑repair operators for large neighborhood search. SPO conditions operator decisions on a compact state representation, enabling state‑dependent behavior, and frames the discovery process as a Stackelberg game where destroy operators act as leaders and repair operators as conditional followers. Experiments on the traveling salesperson and capacitated vehicle routing problems show that SPO outperforms strong baselines, generalizes to larger instances, and exhibits coupled improvement in operator behavior during discovery.

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