Abstract: In order to enhance the optimization efficiency of quantum particle swarm optimization coding based on the probability amplitude，an improved quantum particle swarm optimization is proposed.In the proposed algorithm，the mutation of particle position is performed by quantum Hadamard-gate，the exchange mutation of probability amplitude is improved into the rotation adjustment with better flexibility，and this avoids the loss of population diversity in search space.By studying the relationship among inertia factors，self-factors and global-factors，an adaptive determination method of the global-factors according to the current fitness is proposed.With application of function extremum optimization，the simulation results show that the proposed algorithm is superior to the original one in both search capability and optimization efficiency.

Key words: particle swarm optimization, mutation, adaptive adjustment, optimization algorithm

摘要： 为提高基于概率幅编码的量子粒子群算法的优化效率，提出了一种改进的量子粒子群优化算法。在改进的算法中，采用量子Hadamard门实现粒子位置的变异，将概率幅对换变异改进为更具柔韧性的旋转调整，有效避免了种群在搜索空间中多样性的丢失;通过分析惯性因子、自身因子和全局因子的关系，提出了一种根据粒子当前适应度自适应确定全局因子的方法。以函数极值优化问题为例，仿真结果表明改进算法的搜索能力和优化效率优于原量子粒子群算法。

关键词: 粒子群优化, 变异, 自适应调整, 优化算法