加权微粒群算法在模型配准中的应用

微粒群算法目前已经在很多领域得到了广泛的应用。根据微粒群算法收敛较快的权值范围，建立加权函数，将其运用到速度进化过程中，并在进化过程中分群优化，使得改进的微粒群算法在迭代初期具有较好的全局收敛能力，在迭代后期具有较好的局部收敛能力，从而可以实现维护全局和局部搜索能力的平衡。将该算法运用于散乱点云与三维CAD模型的配准问题中，并与基本微粒群算法进行对比，具有更好的配准结果，迭代收敛更快。

Application of Weight Particle Swarm Algorithm in Model Registration

Particle swarm algorithm is widely applied in many fields. According to weight convergence range, weight function is established and it is applied to the process of velocity evolvement. In the evolvement process, sub-swarms are adopted. So the algorithm has good global convergence ability in the initial iterative stage and has good local convergence ability in the retral iterative stage. This can realize the balance between global search and local search. Weight particle swarm algorithm is used in registration for scattered point cloud and three dimensional CAD model. It has good registration result and fast convergence when it is compared with the basic particle swarm algorithm.