Unleashing the Potential of Large Language Models for Dynamic Multiobjective Optimization
Contribution
Recasts dynamic multiobjective optimization as a prompt-guided time-series prediction problem and
Abstract
To address these challenges, this work introduces a novel approach for DMOPs using large language models (LLMs). Specifically, we reformulate the solution prediction in DMOPs as a time-series prediction problem and guide the LLMs to predict solutions using four carefully crafted prompts. shows that large language models can generate competitive solutions.
Why This Paper Matters
This paper opens a new line of work at the intersection of LLMs and dynamic multiobjective optimization, moving beyond handcrafted transfer heuristics toward prompt-based prediction.
When You May Find This Relevant
Cite this when discussing LLM-assisted dynamic optimization, prompt-based solution prediction, or emerging hybrid optimization frameworks.
- When using large language models for dynamic multiobjective optimization
- When framing dynamic optimization as a prompt-guided prediction task
BibTeX
@article{jiang2026unleashing,
title={Unleashing the Potential of Large Language Models for Dynamic Multiobjective Optimization},
author={Jiang, Min and Wu, Huolong and Wang, Zhenzhong and Yen, Gary G.},
journal={IEEE Transactions on Emerging Topics in Computational Intelligence},
pages={1--12},
year={2026},
doi={10.1109/tetci.2026.3683702},
publisher={IEEE},
}