Training optimization plays a vital role in the development of convolution neural network (CNN). CNNs are hard to train because of the presence of multiple local minima. The optimization problem for a CNN is non-convex, hence, has multiple local minima. If any of the chosen hyper-parameters are not appropriate, it will end up at bad local minima, which leads to poor performance. Hence, proper optimization of the training algorithm for CNN is the key to converge to a good local minimum. Therefore, in this paper, we introduce an evolutionary convolution neural network (ModPSO-CNN) algorithm. The proposed algorithm results in the fusion of modified particle swarm optimization (ModPSO) along with backpropagation (BP) and convolution neural network (CNN). The training of CNN involves ModPSO along with backpropagation (BP) algorithm to encourage performance improvement by avoiding premature convergence and local minima. The ModPSO have adaptive, dynamic and improved parameters, to handle the issues in training CNN. The adaptive and dynamic parameters bring a proper balance between the global and local search ability, while an improved parameter keeps the diversity of the swarm. The proposed ModPSO algorithm is validated on three standard mathematical test functions and compared with three variants of the benchmark PSO algorithm. Furthermore, the performance of the proposed ModPSO-CNN 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, such as CIFAR-10 dataset and face and skin detection dataset.

训练优化在卷积神经网络（CNN）的发展中起着至关重要的作用。由于存在多个局部最小值，因此CNN很难训练。CNN的优化问题是非凸的，因此具有多个局部最小值。如果选择的任何超参数都不适当，则最终将导致局部最小值极小，从而导致性能不佳。因此，针对CNN的训练算法的正确优化是收敛到良好的局部最小值的关键。因此，在本文中，我们介绍了一种进化卷积神经网络（ModPSO-CNN）算法。该算法将改进的粒子群算法（ModPSO）与反向传播（BP）和卷积神经网络（CNN）融合在一起。CNN的训练涉及ModPSO和反向传播（BP）算法，通过避免过早收敛和局部最小值来鼓励性能改善。ModPSO具有自适应，动态和改进的参数，可以处理训练CNN时遇到的问题。自适应和动态参数在全局搜索能力和局部搜索能力之间取得了适当的平衡，而改进的参数则保持了群体的多样性。所提出的ModPSO算法在三个标准数学测试函数上得到了验证，并与基准PSO算法的三个变体进行了比较。此外，还将拟议的ModPSO-CNN的性能与其他训练算法进行了比较，这些训练算法着重于基于分类应用特定的标准问题来分析计算成本，收敛性和准确性，