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

Abstract

High-dimensional electromagnetic optimization problems, such as array antenna design, pose significant challenges due to their complexity and high dimensionality. The Maxwell's equations derived optimization (MEDO) algorithm, a novel optimization method with strong performance in electromagnetics, experiences a decline in efficiency as the problem dimensionality increases. To address these challenges, graphics processing unit (GPU)-accelerated MEDO algorithm with differential grouping (DG-GMEDO) is proposed in this paper, which builds on the MEDO algorithm through the integration of 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 particle swarm optimization and genetic algorithm, 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 both accuracy and efficiency. Furthermore, DG-GMEDO demonstrates significant runtime acceleration and achieves promising results in high-dimensional settings, as validated through its application in array antenna radiation patterns optimization.