An Elite-Guided Large-Scale Multi-Objective Evolutionary Algorithm Driven by Denoising Diffusion Probabilistic Models

Tingting Dang, Wenxuan Fang, Jiaqiang Li, Qiqi Liu, Junhua Gu, Yaochu Jin

Abstract: As the dimensionality of the decision space in multi-objective optimization problems increases, the decision space expands exponentially, presenting significant challenges to the search efficiency of traditional multi-objective evolutionary algorithms in large-scale multi-objective optimization problems. To quickly locate promising search regions in the vast decision space, this paper proposes utilizing denoising diffusion probabilistic models to generate promising solutions, based on which a novel elite-guided large-scale multi-objective evolutionary algorithm is introduced. Specifically, in our proposed method, the population is divided into elite and poor solutions, with each poor solution paired with an elite solution. The elite solutions serve as generation targets, and their paired poor solutions act as conditions during the training of the generative model. Our approach allows the model to not only capture the distribution of elite solutions but also effectively model the evolutionary trajectory from poor solutions to elite solutions. The entire population is used as conditions, and the trained generative model generates ideal positions, which are then updated to produce offspring solutions. Experimental results on large-scale multi-objective benchmark functions demonstrate that the proposed algorithm outperforms four state-of-the-art large-scale multi-objective evolutionary algorithms.

External IDs:dblp:conf/cec/DangFLLGJ25