A Hybrid Quantum Inspired Artificial Bee Colony Algorithm with Dynamic Programming for MIMO Beamforming Optimization

The rapid advancement of modern wireless communication systems has driven an urgent need for complex optimization involving high-dimensional objectives, multiple goals, and non-convex search spaces in multiple-input and multiple-output (MIMO) antenna arrays. However, conventional and quantum-inspired algorithms encounter several limitations, such as an inadequate balance between exploration and exploitation and premature convergence. To address these challenges, this study proposes a hybrid Quantum-Inspired Artificial Bee Colony Algorithm with Dynamic Programming (DP-QABC) for improved MIMO beamforming optimization. The core innovation lies in a hybrid architecture that integrates four quantum-inspired evolution strategies with a diversity-aware dynamic programming controller. The quantum-inspired components, including qubit encoding, quantum rotation gates, entanglement operations, and quantum tunneling, are designed to adaptively expand the global search space. Additionally, a simulated annealing-based acceptance criterion and a cluster-based local exploitation strategy are incorporated to enhance convergence quality and guide the search through complex solution landscapes. The proposed DP-QABC is evaluated comprehensively on both benchmark functions and practical MIMO beamforming scenarios. Experimental results demonstrate that it achieves at least a 40% improvement in search capability and a 8.4%-17.3% reduction in computation time compared to the existing QABC approach. In beamforming applications, an overall performance improvement of approximately 17.1% over QABC is observed, while effectively maintaining high directional gain, interference suppression and low sidelobe levels. These results validate DP-QABC as a robust and efficient solution for complex optimization problems in wireless communications.