Abstract Artificial Neural Networks are commonly used to solve problems in many areas, such as classification, pattern recognition, and image processing. The most challenging and critical phase of an Artificial Neural Networks is related with its training process. The main challenge in the training process is finding optimal network parameters (i.e. weight and biase). For this purpose, numerous heuristic algorithms have been used. One of them is Artificial Algae Algorithm, which has a nature-inspired metaheuristic optimization algorithm. This algorithm is capable of successfully solving a wide variety of numerical optimization problems. In this study, Artificial Algae Algorithm is proposed for training Artificial Neural Network. Ten classification datasets with different degrees of difficulty from the UCI database repository were used to compare the proposed method performance with six well known swarm-based optimization and backpropagation algorithms. The results of the study show that Artificial Algae Algorithm is a reliable approach for training Artificial Neural Networks.

中文翻译：

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用人工藻类算法训练多层感知器

摘要 人工神经网络常用于解决许多领域的问题，如分类、模式识别和图像处理。人工神经网络最具挑战性和关键的阶段与其训练过程有关。训练过程中的主要挑战是找到最佳网络参数（即权重和偏差）。为此，使用了许多启发式算法。其中之一是人工藻类算法，它具有受自然启发的元启发式优化算法。该算法能够成功解决各种数值优化问题。在本研究中，提出了人工藻类算法来训练人工神经网络。使用来自 UCI 数据库存储库的十个不同难度的分类数据集将所提出的方法性能与六种众所周知的基于群的优化和反向传播算法进行比较。研究结果表明，人工藻类算法是训练人工神经网络的可靠方法。