EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization

TL;DR

EmoDM introduces a diffusion model that learns to generate high-quality solutions for multi-objective optimization problems, significantly reducing the need for expensive function evaluations and demonstrating strong generalization and efficiency.

Contribution

This work is the first to apply a diffusion model to evolutionary multi-objective optimization, enabling efficient solution generation without extensive search.

Findings

Outperforms state-of-the-art algorithms in search performance.

Reduces computational cost by minimizing function evaluations.

Generalizes well to unseen large-scale problems.

Abstract

Evolutionary algorithms have been successful in solving multi-objective optimization problems (MOPs). However, as a class of population-based search methodology, evolutionary algorithms require a large number of evaluations of the objective functions, preventing them from being applied to a wide range of expensive MOPs. To tackle the above challenge, this work proposes for the first time a diffusion model that can learn to perform evolutionary multi-objective search, called EmoDM. This is achieved by treating the reversed convergence process of evolutionary search as the forward diffusion and learn the noise distributions from previously solved evolutionary optimization tasks. The pre-trained EmoDM can then generate a set of non-dominated solutions for a new MOP by means of its reverse diffusion without further evolutionary search, thereby significantly reducing the required function evaluations. To enhance the scalability of EmoDM, a mutual entropy-based attention mechanism is introduced to capture the decision variables that are most important for the objectives. Experimental results demonstrate the competitiveness of EmoDM in terms of both the search performance and computational efficiency compared with state-of-the-art evolutionary algorithms in solving MOPs having up to 5000 decision variables. The pre-trained EmoDM is shown to generalize well to unseen problems, revealing its strong potential as a general and efficient MOP solver.