Grid search is the most effective method for tuning hyperparameters in machine learning (ML). However, it has high computational complexity, and is not appropriate when here are many hyperparameters to be tuned. In this paper, two novel suboptimal grid search methods are presented, which search the grid marginally and alternatively. In order to show the efficiency of hyperparameter tuning by the proposed methods four datasets are used. Two datasets were collected by simulating FSO and fiber OC links by MATLAB software, and two other datasets were collected by experimental setups for FSO and fiber OC links in Optisystem software. Results indicate that despite greatly reducing computational complexity, the proposed methods achieve a favorable performance. The proposed structures are compared with some of the recently published most relevant works, and the efficiency of the proposed methods is proved.

网格搜索是在机器学习（ML）中调整超参数的最有效方法。但是，它具有很高的计算复杂性，并且在这里有许多要调整的超参数时不适合使用。本文提出了两种新颖的次优网格搜索方法，分别对网格进行边际搜索。为了显示所提出方法的超参数调整效率，使用了四个数据集。通过使用MATLAB软件模拟FSO和光纤OC链路收集了两个数据集，并通过在Optisystem软件中通过FSO和光纤OC链路的实验设置收集了另外两个数据集。结果表明，尽管大大降低了计算复杂度，但所提出的方法仍具有良好的性能。将拟议的结构与最近发布的一些最相关的作品进行了比较，