Abstract: To improve the performance of particle swarm optimization，an adaptive quantum particle swarm optimization algorithm is proposed.In proposed algorithm，the position of particles is described by the phase of quantum bits，and the position mutation of particles is achieved by Pauli-Z gates.By studying the relationship among inertia factors，self-factors and global-
factors，an adaptive determination of the global-factors is proposed.Taking function extremum optimizing and samples clustering for example，the experimental results show that the proposed algorithm is obviously superior to the standard particle swarm optimization.

Key words: particle swarm optimization, phase encoding, adaptive adjustment, optimization algorithm

摘要： 为提高粒子群算法的优化性能，提出了一种基于相位编码的量子粒子群算法。用量子比特的相位描述粒子的空间位置，用Pauli-Z门实现粒子位置的变异。通过研究惯性因子、自身因子和全局因子的关系，提出了全局因子的自适应确定方法。以典型函数的极值优化和样本聚类问题为例的实验结果表明，该方法明显优于普通粒子群算法。

关键词: 粒子群优化, 相位编码, 自适应调整, 优化算法