Generative Model-Driven Large-Scale Dynamic Multi-Objective Evolutionary Optimization

In practical applications, dynamic multi-objective optimization problems inevitably face large-scale scenarios. Massive search space and high-dimensional historical data pose some serious challenges to existing prediction-based change response mechanisms, which affects their ability in maintaining population diversity and accurately predicting convergent solutions. In this paper, we propose a generative model-driven approach that trains a generative model for a new environment only through historical Pareto optimal sets. It can generate both convergent and diverse population for the new environment. We show that by introducing a new training scheme and loss function for adversarial autoencoder, the training of the generative model can achieve stable convergence under high-dimensional coupled data conditions. In addition, the trained generative model can maintain the diversity of generative candidate solutions by smooth sampling in the latent space. Extensive experiments were conducted on a typical dynamic multi-objective testing suite with problem settings ranging from 30 to 600 dimensions. These experimental results demonstrate that the optimization performance of the proposed approach outperforms those of the existing state-of-the-art designs.