Abstract
This study proposes NIRMAL (Novel Integrated Robust Multi-Adaptation Learning), a novel optimization algorithm that combines multiple strategies inspired by the movements of the chess piece. These strategies include gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We carefully evaluated NIRMAL against two widely used and successful optimizers, Adam and SGD with Momentum, on four benchmark image classification datasets: MNIST, FashionMNIST, CIFAR-10, and CIFAR-100. The custom convolutional neural network (CNN) architecture is applied on each dataset. The experimental results show that NIRMAL achieves competitive performance, particularly on the more challenging CIFAR-100 dataset, where it achieved a test accuracy of 45.32\%and a weighted F1-score of 0.4328. This performance surpasses Adam (41.79\% accuracy, 0.3964 F1-score) and closely matches SGD with Momentum (46.97\% accuracy, 0.4531 F1-score). Also, NIRMAL exhibits robust convergence and strong generalization capabilities, especially on complex datasets, as evidenced by stable training results in loss and accuracy curves. These findings underscore NIRMAL's significant ability as a versatile and effective optimizer for various deep learning tasks.
Abstract (translated)
这项研究提出了NIRMAL（新颖的综合鲁棒多适应学习），这是一种新的优化算法，结合了多种策略，这些策略受到了国际象棋棋子移动方式的启发。这些策略包括梯度下降、动量、随机扰动、自适应学习率和非线性变换。我们仔细地将NIRMAL与两种广泛使用且成功的优化器——Adam和带动量的SGD，在四个基准图像分类数据集上进行了比较：MNIST、FashionMNIST、CIFAR-10 和 CIFAR-100。针对每个数据集，应用了自定义的卷积神经网络（CNN）架构。实验结果表明，NIRMAL实现了具有竞争力的表现，特别是在更具挑战性的CIFAR-100 数据集中，它的测试准确率为45.32%，加权F1分数为0.4328。这一性能超过了Adam（准确率 41.79%、F1 分数 0.3964）并且接近于带动量的SGD（准确率 46.97%、F1 分数 0.4531）。此外，NIRMAL展示了鲁棒收敛和强大的泛化能力，尤其是在复杂数据集上，这一点通过损失和精度曲线中的稳定训练结果得到了证实。这些发现强调了 NIRMAL 在各种深度学习任务中作为一种灵活且有效的优化器的重要性。
URL
https://arxiv.org/abs/2508.04293
