Multiobjective evolutionary algorithms (MOEAs) are major methods for solving
multiobjective optimization problems (MOPs). Many MOEAs have been proposed in
the past decades, of which the operators need carefully handcrafted design with
domain knowledge. Recently, some attempts have been made to replace the
manually designed operators in MOEAs with learning-based operators (e.g.,
neural network models). However, much effort is still required for designing
and training such models, and the learned operators might not generalize well
to solve new problems. To tackle the above challenges, this work investigates a
novel approach that leverages the powerful large language model (LLM) to design
MOEA operators. With proper prompt engineering, we successfully let a general
LLM serve as a black-box search operator for decomposition-based MOEA (MOEA/D)
in a zero-shot manner. In addition, by learning from the LLM behavior, we
further design an explicit white-box operator with randomness and propose a new
version of decomposition-based MOEA, termed MOEA/D-LO. Experimental studies on
different test benchmarks show that our proposed method can achieve competitive
performance with widely used MOEAs. It is also promising to see the operator
only learned from a few instances can have robust generalization performance on
unseen problems with quite different patterns and settings. The results reveal
the potential benefits of using pre-trained LLMs in the design of MOEAs.

使用预训练的大型语言模型（LLM）设计的多目标进化算法（MOEA）运算符显示出潜在的优势，并能在不同模式和设置下具有稳健的泛化性能。