LLM-Driven Customizable Fireworks Algorithm for Diverse Optimization Tasks

Designing specific optimizers for specific optimization tasks has been an important but difficult task due to the No Free Lunch theorem. Nowadays, large language models have been widely applied to automatic algorithm design, but much of the current work still focuses on the design of heuristic rules. In this work, we propose a framework for the evolution of optimizer (Fireworks Algorithm) based on large language models, thereby achieving the customization of specific Fireworks Algorithms for different specific problems. In the experimental part, we use GFWA as the starting point for evolution and achieve significant improvement in optimization on both CEC2013 and CEC2017, while on many engineering problems with very complex constraints, the LLM-driven Fireworks Algorithm generates solutions that not only satisfy all constraints but also have higher quality compared to previous work, which underscores the great potential of the large language model in optimizing the optimizer . It is worth noting that this framework diminishes the reliance on expert knowledge, which was previously emphasized.