Unlocking Large Language Model Capabilities for Expensive Dynamic Multiobjective Optimization

Dynamic multiobjective optimization problems (DMOPs) involve optimizing multiple, often conflicting, goals that change over time. In recent years, numerous algorithms have been developed to track these moving optimal solutions, which rely on machine learning and historical optimization data, struggle with data inefficiency-a critical bottleneck in expensive DMOPs where objective evaluations are resource-intensive and historical data is scarce. This paper investigates the capabilities of large language models (LLMs) to address this challenge. The core idea is to reframe the solution prediction process in DMOPs as a time-series forecasting task. LLMs are then guided to predict solutions using carefully constructed prompts. This prompt-based method enables LLMs to effectively predict solutions with only a limited amount of historical data. Our experiments on multiple benchmark problems show the efficacy of LLMs in handling expensive DMOPs, offering a promising direction for efficient and adaptive dynamic optimization.