Hierarchical Diffusion Teaching-Learning-Based Optimizer with Variational Autoencoder for Mobile Edge Computing System Optimization

Evolutionary computation for addressing high-dimensional expensive problems (HEPs) characterized by both high-dimensional decision variables and resource-intensive evaluations is an important area. In this study, we introduce a novel approach, namely the Hierarchical Diffusion Teaching-learning-based Optimizer with Variational autoencoder (HDTOV). Firstly, we employ a variational autoencoder to reduce problem dimensions and facilitate the learning of the optimization process. Secondly, we employ a hierarchical population reconstruction strategy to enhance population diversity. Lastly, to exploit the population more effectively, we implement a diffusion mechanism to prevent premature convergence. The proposed method is validated through experiments on a real-life optimization problem arising from the operation of mobile edge computing systems. The experimental results demonstrate the efficacy and efficiency of HDTOV in addressing HEPs by its outperforming the state of the art.