In this paper, we borrow the large language model (LLM) ChatGPT-3.5 to automatically and quickly design a new metaheuristic algorithm (MA) with only a small amount of input. The novel animal-inspired MA named zoological search optimization (ZSO) draws inspiration from the collective behaviors of animals for solving continuous optimization problems. Specifically, the basic ZSO algorithm involves two search operators: the prey-predator interaction operator and the social flocking operator to balance exploration and exploitation well. Besides, the standard prompt engineering framework CRISPE (i.e., Capacity and Role, Insight, Statement, Personality, and Experiment) is responsible for the specific prompt design. Furthermore, we designed four variants of the ZSO algorithm with slight human-interacted adjustment. In numerical experiments, we comprehensively investigate the performance of ZSO-derived algorithms on CEC2014 benchmark functions, CEC2022 benchmark functions, and six engineering optimization problems. 20 popular and state-of-the-art MAs are employed as competitors. The experimental results and statistical analysis confirm the efficiency and effectiveness of ZSO-derived algorithms. At the end of this paper, we explore the prospects for the development of the metaheuristics community under the LLM era.

翻译：本文借助大型语言模型（LLM）ChatGPT-3.5，通过少量输入自动快速设计一种新型元启发式算法（MA）。该受动物行为启发的算法——命名为动物搜索优化（ZSO），通过模拟动物群体行为求解连续优化问题。具体而言，基础ZSO算法包含两个搜索算子：捕食者-猎物交互算子与社会集群算子，以实现探索与开发能力的良好平衡。此外，标准提示工程框架CRISPE（即能力与角色、洞察、陈述、个性及实验）负责具体提示设计。我们通过少量人工交互调整，进一步设计了ZSO算法的四种变体。数值实验中，我们在CEC2014基准函数、CEC2022基准函数及六个工程优化问题上全面研究了ZSO衍生算法的性能，并选取20种主流及最新元启发式算法作为对比。实验结果与统计分析证实了ZSO衍生算法的有效性与高效性。本文最后探讨了大语言模型时代下元启发式算法领域的发展前景。