In this paper, we address the challenges of achieving both low complexity and high resolution in direction-of-arrival (DOA) sensing for impinging signals on an antenna sensor array. We propose a phase alignment (PA) method enhanced with particle swarm optimization (PSO) algorithms for hybrid analog and digital (HAD) massive multiple-input multiple-output (MIMO) receiving arrays. This approach aims to reduce costs and improve energy efficiency while supporting high network access capability and meeting the demands of massive internet of things (mIoT). These methods include linear search techniques such as traditional analog phase alignment, HAD phase alignment (HADPA), and hybrid digital and analog phase alignment. The objective is to estimate the DOA for multiple signal sources. It has been demonstrated that the search complexity and estimation accuracy of the PA methods are contingent upon the number of search grids employed during the peak searching process, which is time-consuming, and the required number of search grids is not readily discernible. Furthermore, PSO algorithms can be employed to address these issues in the context of DOA estimation. Additionally, the quantum-behaved PSO (QPSO) does not require velocity vectors, results in a reduction in the number of parameters, and is simpler to implement. We also present a DOA search method based on QPSO, which is designed to reduce further the computational complexity. Finally, several computer simulation results are provided for illustrative and comparative purposes.