Abstract: To increase global search ability and escape from local minima,the mutation mechanism has been introduced into Quantum-behaved Particle Swarm Optimization（QPSO）,namely based on the characteristic of QPSO algorithm,the variable of gbest and mbest（Mean Best Position） is mutated with Cauchy distribution respectively.It is called Quantum-behaved Particle Swarm Optimization algorithm with mutation operator（MQPSO）.Through the experiment of a typical Needle-in-a-haystack problem,the proposed algorithm has showed its better global optimal ability and its faster convergence ability.Based on the above,the algorithm has been applied to identify system parameter.The identification results have showed that this method has the advantages of high parameter identification precision,strong ability of resistance to the noise,good input signal generality and identification of the nonlinear system,so it has important practical values．

摘要： 为了增加全局搜索能力,避免陷入局部最小,在量子粒子群优化算法（QPSO）中引入变异机制,即基于QPSO的特点,用Cauchy分布分别对全局最优和所有个体极值的平均值进行变异。该算法称为带变异算子的量子粒子群优化算法（MQPSO）。通过对一典型的大海捞针类（NiH）问题的试验,证明了MQPSO在全局优化和快速收敛能力上有较大的提高。在此基础上将该算法应用于系统参数辨识中,辨识结果表明该方法具有参数辨识精度高,抗噪声能力强,对输入信号通用性强,也适用于非线性系统参数辫识,具有重要的工程应用价值。