Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization

TL;DR

This paper introduces a novel approach that integrates a fine-tuned large language model into evolutionary algorithms to enhance convergence speed in constrained multi-objective optimization problems.

Contribution

It presents a method for finetuning an LLM with prompt engineering to serve as a search operator, improving solution quality and convergence in constrained multi-objective optimization.

Findings

LLM-aided search accelerates convergence speed.

The approach outperforms existing evolutionary algorithms.

Significant improvements in solution quality on benchmark problems.

Abstract

Evolutionary algorithms excel in solving complex optimization problems, especially those with multiple objectives. However, their stochastic nature can sometimes hinder rapid convergence to the global optima, particularly in scenarios involving constraints. In this study, we employ a large language model (LLM) to enhance evolutionary search for solving constrained multi-objective optimization problems. Our aim is to speed up the convergence of the evolutionary population. To achieve this, we finetune the LLM through tailored prompt engineering, integrating information concerning both objective values and constraint violations of solutions. This process enables the LLM to grasp the relationship between well-performing and poorly performing solutions based on the provided input data. Solution's quality is assessed based on their constraint violations and objective-based performance. By leveraging the refined LLM, it can be used as a search operator to generate superior-quality solutions. Experimental evaluations across various test benchmarks illustrate that LLM-aided evolutionary search can significantly accelerate the population's convergence speed and stands out competitively against cutting-edge evolutionary algorithms.