Abstract: In the view of the Quantum-mechanical field，a Quantum-behaved Particle Swarm Optimization algorithm is proposed by Sun et，which outperforms traditional PSOs in search ability as well as having less parameter.This paper focuses on discussing how to select parameter when QPSO is practically applied.After the QPSO algorithm is described，the experiment results of stochastic simulation are given to show how the selection of the parameter value influences the convergence of the particle in QPSO.Finally，two parameter control methods are presented and experiment results on the benchmark functions testify their efficiency.

Key words: optimization, evolutionary search technique, particle swarm, quantum-behaved

摘要： Sun等人从量子力学的角度提出了具有量子行为的粒子群优化算法，它在搜索能力上优于传统的PSO算法，自适应参数的数目也比之较少。集中讨论了应用QPSO如何选择自适应参数的问题。介绍了QPSO算法，给出了随机模拟的实验结果，从而看到了参数值的选择如何影响粒子在QPSO中的收敛。最后，介绍了两种自适应参数控制方法和标准测试函数的实验结果。

关键词: 优化, 进化计算, 粒子群, 量子行为