GPU-Accelerated MEDO Algorithm with Differential Grouping (DG-GMEDO) for High-Dimensional Electromagnetic Optimization Problems

High-dimensional Electromagnetic Optimization Problems, such as array antenna design, present significant challenges due to their complexity and high dimensionality. The MEDO algorithm, a novel optimization method with strong performance in electromagnetics, suffers a decline in efficiency as the problem dimensionality increases. To address these challenges, DG-GMEDO is proposed in this paper, which builds on the MEDO algorithm by integrating an enhanced differential grouping strategy and GPU-based parallel acceleration. This approach allows for more effective management of variable interactions while leveraging high computational speeds. Comparative evaluations with traditional algorithms like PSO and GA, as well as state-of-the-art methods such as MAES2-EDG, GTDE, RCI-PSO, and CCFR2-IRRG, highlight its competitive performance in terms of accuracy and efficiency. Furthermore, DG-GMEDO demonstrates significant runtime acceleration and achieves promising results in high-dimensional settings, as validated through its application in optimizing array antenna radiation patterns.