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

arXiv: https://arxiv.org/abs/2403.16417 · HTML: https://mlsanity.com/paper/2403.16417/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2024.

## Built on by (most cited first, 8 of 8)

- [Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms](https://mlsanity.com/paper/2410.20848/) (2024)
- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems](https://mlsanity.com/paper/2505.15741/) (2025)
- [Large Language Models in Operations Research: Methods, Applications, and Challenges](https://mlsanity.com/paper/2509.18180/) (2025)
- [Beyond the Node: Clade-level Selection for Efficient MCTS in Automatic Heuristic Design](https://mlsanity.com/paper/2602.00549/) (2026)
- [Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows](https://mlsanity.com/paper/2505.04354/) (2025)
- [RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework](https://mlsanity.com/paper/2505.13808/) (2025)
- [Discrimination by LLMs: Cross-lingual Bias Assessment and Mitigation in Decision-Making and Summarisation](https://mlsanity.com/paper/2509.09735/) (2025)

## Abstract (arXiv)

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.