LLM-Distilled Surrogate Model for Expensive Multi-objective Optimization

In expensive multi-objective optimization problems (EMOPs), surrogate-assisted evolutionary algorithms (SAEAs) have become a predominant approach. However, surrogate models often suffer from degraded performance due to limited training data—a prevalent and critical challenge in this domain. To address this issue, we propose a novel framework named DuSiM that leverages the capabilities of Large Language Models (LLMs) to assist surrogate model training. Specifically, DuSiM uses LLMs to generate additional high-quality training data, which enhances the surrogate model’s approximation accuracy despite the scarcity of training data. Specifically, DuSiM first uses the surrogate model to guide the prompt-feedback tuning of the LLM. Once the LLM adapts to predicting evaluation function values and uncertainties, it subsequently generates a substantial amount of high-quality synthetic data to assist in training the surrogate model. To evaluate the effectiveness of DuSiM, we compare it with three state-of-the-art algorithms on various problems. Experimental results demonstrate that our framework can accelerate the convergence of SAEAs and outperforms other algorithms in most cases.