 The proposed hybrid particle swarm optimization, HIPSO, algorithm is designed to improve the performance of Latin hypercube design, LHD. It increases the speed and strengthens the local search to ensure the diversity of the population and reduce the oscillations of the PSO algorithm. Additionally, it uses the ranked-ordered value, ROV, rule to discretize the PSO algorithm and the minimum point distance, MPD, method to solve the oscillation problem. Finally, local and global searches are conducted to find the near-optimal HLHD. Comparative tests demonstrate that the proposed HIPSO algorithm outperforms other algorithms in terms of finding the near-optimal HLHD. This article was authored by Zhixin Su, Dongqin Xia, Nuoyong, and others.