This paper presents an improved teaching learning-based whale optimization algorithm (TSWOA) used the simplex method. First of all, the combination of WOA algorithm and teaching learning-based algorithm not only achieves a better balance between exploration and exploitation of WOA, but also makes whales have self-learning ability from the biological background, and greatly enriches the theory of the original WOA algorithm. Secondly, the WOA algorithm adds the simplex method to optimize the current worst unit, averting the agents to search at the boundary, and increasing the convergence accuracy and speed of the algorithm. To evaluate the performance of the improved algorithm, the TSWOA algorithm is employed to train the multi-layer perceptron (MLP) neural network. It is a difficult thing to propose a well-pleasing and valid algorithm to optimize the multi-layer perceptron neural network. Fifteen different data sets were selected from the UCI machine learning knowledge and the statistical results were compared with GOA, GSO, SSO, FPA, GA and WOA, severally. The statistical results display that better performance of TSWOA compared to WOA and several well-established algorithms for training multi-layer perceptron neural networks.

中文翻译：

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基于教学学习的鲸鱼优化算法在多层感知器神经网络训练中的应用

本文提出了一种改进的基于单纯形方法的基于教学学习的鲸鱼优化算法（TSWOA）。首先，将WOA算法与基于教学的学习算法相结合，不仅可以在WOA的探索与开发之间达到更好的平衡，而且使鲸鱼具有生物学背景的自学能力，极大地丰富了原著的理论。 WOA算法。其次，WOA算法增加了单纯形法来优化当前最差单元，避免了在边界搜索智能体，提高了算法的收敛精度和速度。为了评估改进算法的性能，采用TSWOA算法训练多层感知器（MLP）神经网络。提出一种令人愉悦且有效的算法来优化多层感知器神经网络是一件困难的事情。从UCI机器学习知识中选择15种不同的数据集，并将统计结果分别与GOA，GSO，SSO，FPA，GA和WOA进行比较。统计结果表明，与WOA相比，TSWOA的性能更好，并且在训练多层感知器神经网络方面有几种公认的算法。