Design of Mutation Operators in Fireworks Algorithm Assisted by Large Language Models
Yifan Liu, Ying Tan · 2025
This paper proposes the Large Language Model Assisted Operator Design Framework (LLM-AODF), a novel approach for the automatic generation and optimization of operators within the Fireworks Algorithm (FWA). The framework involves inputting the source code of the fireworks algorithm into a large language model to generate new mutation operators, followed by multiple rounds of iterative optimization of the operator design. By leveraging large language models, LLMAODF can fully automate the improvement of mutation operators in the FWA. The effectiveness of the framework is validated through experiments using the CEC2013 black-box optimization test suite. Experimental results indicate that mutation operators generated by different large language models can improve the performance of the FWA to different extents. An in-depth analysis of the mutation operators generated by various models revealed that large language models can produce complex and diverse mutation strategies through multiple iterations, thereby enhancing the algorithm’s exploration and exploitation capabilities. The framework can be easily extended to different algorithms and various problems, offering a novel approach for the automated design of algorithms.