Leveraging large language model to generate a novel metaheuristic algorithm with CRISPE framework

R Zhong and YF Xu and C Zhang and J Yu, CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 27, 13871-13896 (2024).

DOI: 10.1007/s10586-024-04654-6

In this paper, we introduce the large language model (LLM) ChatGPT-3.5 to automatically and intelligently generate a new metaheuristic algorithm (MA) according to the standard prompt engineering framework CRISPE (i.e., Capacity and Role, Insight, Statement, Personality, and Experiment). 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. 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.

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