  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.
