Metaheuristics and Large Language Models Join Forces: Towards an Integrated Optimization Approach

• URL: http://arxiv.org/abs/2405.18272v1
• Date: Tue, 28 May 2024 15:23:46 GMT
• Title: Metaheuristics and Large Language Models Join Forces: Towards an Integrated Optimization Approach
• Authors: Camilo Chacón Sartori, Christian Blum, Filippo Bistaffa, Guillem Rodríguez Corominas,
• Abstract summary: This paper introduces a novel approach that leverages Large Language Models (LLMs) as pattern recognition tools to improve metaheuristics (MHs) The resulting hybrid method, tested in the context of a social network-based optimization problem, outperforms existing state-of-the-art approaches.
• Score: 2.2124180701409233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since the rise of Large Language Models (LLMs) a couple of years ago, researchers in metaheuristics (MHs) have wondered how to use their power in a beneficial way within their algorithms. This paper introduces a novel approach that leverages LLMs as pattern recognition tools to improve MHs. The resulting hybrid method, tested in the context of a social network-based combinatorial optimization problem, outperforms existing state-of-the-art approaches that combine machine learning with MHs regarding the obtained solution quality. By carefully designing prompts, we demonstrate that the output obtained from LLMs can be used as problem knowledge, leading to improved results. Lastly, we acknowledge LLMs' potential drawbacks and limitations and consider it essential to examine them to advance this type of research further.

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