Leveraging Large Language Model to Generate a Novel Metaheuristic Algorithm with CRISPE Framework

TL;DR

This paper introduces a novel animal-inspired metaheuristic algorithm called ZSO, generated using ChatGPT-3.5 with the CRISPE prompt framework, and demonstrates its competitive performance on standard benchmarks and engineering problems.

Contribution

It presents a new metaheuristic algorithm created by leveraging large language models with a structured prompt framework, marking a novel integration of AI and optimization.

Findings

ZSO algorithms perform well on benchmark functions.

ZSO-derived algorithms outperform several state-of-the-art methods.

The CRISPE framework effectively guides LLM-based algorithm design.

Abstract

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.