Training of the convolution neural network (CNN) is a problem of global optimisation. This study proposed a hybrid modified particle swarm optimisation (MPSO) and conjugate gradient (CG) algorithm for efficient training of CNN. The training involves MPSO–CG to avoid trapping in local minima. Particularly, improvements in the MPSO by introducing a novel approach for control parameters, improved parameters updating criteria, a novel parameter in the velocity update equation, and fusion of the CG allows handling the issues in training CNN. In this study, the authors validate the proposed MPSO algorithm on three benchmark mathematical test functions and also compared with three different variants of the baseline particle swarm optimisation algorithm. Furthermore, the performance of the proposed MPSO–CG is also compared with other training algorithms focusing on the analysis of computational cost, convergence, and accuracy based on a standard problem specific to classification applications on CIFAR-10 dataset and face and skin detection dataset.

中文翻译：

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基于优化的进化卷积神经网络的视觉分类应用训练

卷积神经网络（CNN）的训练是全球优化的问题。这项研究提出了一种混合改进的粒子群优化算法（MPSO）和共轭梯度（CG）算法，用于CNN的有效训练。培训涉及MPSO–CG，以避免陷入局部最小值。特别地，通过引入控制参数的新方法，改进的参数更新标准，速度更新方程式中的新参数以及CG的融合，MPSO的改进允许处理训练CNN的问题。在这项研究中，作者在三种基准数学测试函数上验证了所提出的MPSO算法，并与基线粒子群优化算法的三种不同变体进行了比较。此外，