中文摘要：

该文提出一种量子白适应粒子群优化算法，该算法中，粒子位置的编码采用量子比特实现，利用粒子飞行轨迹信息动态更新量子比特的状态，并引入量子非门实现变异操作以避免陷入局部最优。用该算法训练神经网络，实现了径向基函数（RJBF）神经网络参数优化，建立了基于量子自适应粒子群优化RBF神经网络算法的网络流量预测模型。对真实网络流量的预测结果表明，该方法的收敛速度和预测精度均要优于传统RBF神经网络法、粒子群-RBF神经网络法、混合粒子群-RBF神经网络法和自适应粒子群-RBF神经网络法，并且预测效果不易受时间尺度变化的影响。

英文摘要：

A novel Quantum Adaptive Particle Swarm Optimization （QAPSO） method is proposed. In this algorithm, the position encoding of the particle is achieved with quantum bits, and the state of quantum bit is updated dynamically with particle trajectory information. Then the mutation operation is performed by quantum non-gate to avoid falling into local optimum, which increases the diversity of particles. Afterwards, the Radial Basis Function （RBF） neural network is trained with QAPSO to implement the optimization of RBF neural network parameters. The network traffic prediction model is established based on the Quantum Adaptive Particle Swarm Optimization and RBF Neural Network （QAPSO~RBFNN）. Forecasting results on real network traffic demonstrate that the convergence speed of the proposed method is faster and prediction accuracy is more accurate than that of traditional RBF neural network, the Particle Swarm Optimization and RBFNN （PSO-RBFNN）, Hybrid Particle Swarm Optimization and RBFNN （HPSO-RBFNN）, Adaptive Particle Swarm Optimization and RBF Neural Network （APSO-RBFNN）. Furthermore, the forecasting effect of this method is stable on different scales