  Evolutionary algorithms excel in solving complex optimization problems,
especially those with multiple objectives. However, their stochastic nature can
sometimes hinder rapid convergence to the global optima, particularly in
scenarios involving constraints. In this study, we employ a large language
model (LLM) to enhance evolutionary search for solving constrained
multi-objective optimization problems. Our aim is to speed up the convergence
of the evolutionary population. To achieve this, we finetune the LLM through
tailored prompt engineering, integrating information concerning both objective
values and constraint violations of solutions. This process enables the LLM to
grasp the relationship between well-performing and poorly performing solutions
based on the provided input data. Solution's quality is assessed based on their
constraint violations and objective-based performance. By leveraging the
refined LLM, it can be used as a search operator to generate superior-quality
solutions. Experimental evaluations across various test benchmarks illustrate
that LLM-aided evolutionary search can significantly accelerate the
population's convergence speed and stands out competitively against
cutting-edge evolutionary algorithms.
