Ensemble Surrogate-Assisted Optimization for Dynamic Multi-UAV Enabled Wireless Powered Communication Network Problem

Abstract

High-dimensional Expensive Mixed-Variable Optimization Problems (HEMVOPs) are widely prevalent in the design of complex engineering systems, such as Unmanned Aerial Vehicle (UAV) communications and smart grids. These problems are notoriously challenging due to the curse of dimensionality, strong coupling between continuous and discrete variables, and prohibitive cost of each physical simulation evaluations. To address these issues, this work proposes an Ensemble Surrogate-assisted Hybrid Search framework (ESHS). First, a data-driven importance-aware dimension expansion mechanism is proposed to relieve the curse of dimensionality. In addition, an adaptive heterogeneous ensemble model is constructed to reduce simulation cost. Extensive experiments are conducted on two highly complex real-world UAV-assisted network communication problems. The results demonstrate that, under strictly limited evaluation budgets, ESHS outperforms existing stateof-the-art algorithms in both convergence efficiency and global search accuracy.