Autonomous Multi-Objective Optimization Using Large Language Model

TL;DR

This paper introduces an LLM-based framework that autonomously designs evolutionary algorithm operators for multi-objective optimization, reducing reliance on domain expertise and improving adaptability and performance across diverse problems.

Contribution

The paper presents a novel LLM-driven approach to automatically generate and refine EA operators for MOPs, enhancing flexibility and efficiency compared to traditional methods.

Findings

Framework achieves superior performance on various MOPs

Reduces need for expert intervention in operator design

Demonstrates robustness across multiple problem categories

Abstract

Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives. Traditional evolutionary algorithms (EAs), though effective, often rely on domain-specific expertise and iterative fine-tuning, hindering adaptability to unseen MOPs. In recent years, the advent of Large Language Models (LLMs) has revolutionized software engineering by enabling the autonomous generation and refinement of programs. Leveraging this breakthrough, we propose a new LLM-based framework that autonomously designs EA operators for solving MOPs. The proposed framework includes a robust testing module to refine the generated EA operator through error-driven dialogue with LLMs, a dynamic selection strategy along with informative prompting-based crossover and mutation to fit textual optimization pipeline. Our approach facilitates the design of EA operators without the extensive demands for expert intervention, thereby speeding up the innovation of EA operators. Empirical studies across various MOP categories validate the robustness and superior performance of our proposed framework.