Compare the test functions.

The setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optimal value of neural network parameters adaptively, and proposes an ATHGS-GoogleNet model. Firstly, adaptive weights and chaos mapping were integrated into the hunger search algorithm to construct a new algorithm, ATHGS. Secondly, the improved ATHGS algorithm was used to optimize the parameters of GoogleNet to construct a new model, ATHGS-GoogleNet. Finally, in order to verify the effectiveness of the proposed algorithm ATHGS and the model ATHGS-GoogleNet, a comparative experiment was set up. Experimental results show that the proposed algorithm ATHGS shows the best optimization performance in the three engineering experimental designs, and the accuracy of the proposed model ATHGS-GoogleNet reaches 98.1%, the sensitivity reaches 100%, and the precision reaches 99.5%.

参数值的设置会直接影响神经网络的性能，而人工调参不仅速度缓慢，还难以寻找到最优的参数组合。基于此，本文采用改进的饥饿游戏搜索算法（Hunger Games Search Algorithm）自适应寻得神经网络参数的最优组合，并提出ATHGS-GoogleNet模型。首先，将自适应权重与混沌映射融入该搜索算法，构建出全新的ATHGS算法；其次，利用改进后的ATHGS算法对GoogleNet的参数进行优化，构建得到新型模型ATHGS-GoogleNet；最后，为验证所提ATHGS算法与ATHGS-GoogleNet模型的有效性，本文设置了对比实验。实验结果表明，所提ATHGS算法在三项工程实验设计中均展现出最优的优化性能，所构建的ATHGS-GoogleNet模型准确率可达98.1%、灵敏度达100%、精确率达99.5%。