ACCQPSO:一种改进的量子粒子群优化算法及其应用

ACCQPSO:An Improved Quantum Particle Swarm Optimization Algorithm and Its Applications

针对量子粒子群优化算法前期易陷入局部极值点、后期寻优精度不高等问题,文章提出一种自适应交叉算子的混沌量子粒子群优化算法,并将其应用于BP神经网络超参数寻优。首先,利用Logistics映射初始种群为混沌序列进行最优解搜索,增强初始种群的随机性与遍历性,提高算法寻优能力;然后,通过纵向交叉操作进行种群中个体的信息交换,并引入自适应交叉概率公式,增加种群多样性,提高算法的寻优精度;最后,在实验中,一方面,选取8个函数在高低两个维度进行验证,同时进行Wilcoxon秩和检验分析以及消融实验,验证该算法相较其他算法的有效性;另一方面,通过算法优化BP神经网络应用到网络安全态势预测任务中,实验结果表明该算法收敛速度相较于对比算法有大幅度提升。

In order to solve the problems of quantum particle swarm optimization(QPSO),such as easy to fall into local extreme point in the early stage and low accuracy in the later stage,a chaotic quantum particle swarm optimization algorithm with adaptive crossover operator(ACCQPSO)was proposed and used in the hyper-parameter optimization of the BP neural network.Firstly,the initial population of Logistics map was used as chaotic sequence to search the optimal solution,which enhanced the randomness and ergodicity of the initial population and improved the optimization ability of the algorithm.Secondly,the information of individuals in the population was exchanged through vertical crossover operation,and the adaptive crossover probability formula was introduced to increase the population diversity and improved the optimization accuracy of the algorithm.In the experiment,on the one hand,eight functions were selected for validation in both high and low dimensions,while Wilcoxon rank sum test analysis and ablation experiments were performed to verify the effectiveness of the algorithm compared to other algorithms;on the other hand,the parameters optimization of BP neural network were applied to the network security situation prediction task,and the results show that the convergence speed is greatly improved compared with the contrast algorithm.