Abstract:

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.

Key words: quantum particle swarm optimization algorithm, chaotic mapping, crossover operator, adaptive adjustment strategy, BP neural network