Software usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.

研究人员通常使用软件可用性来参考分层软件可用性模型，它是用户体验和软件质量的重要方面。因此，软件可用性的评估是用于管理和调节软件的基本参数。但是，很难为该问题建立精确的评估方法。许多研究人员已经提出了许多可用性因素，每个因素都涵盖了一系列不同的因素，以提高软件的用户友好程度。因此，正确确定特征的选择至关重要。本文提出了一种创新的元启发式算法，用于选择分层软件模型中最重要的特征。基于层次结构的可用性模型是对因素的详尽解释，属性及其在不同级别的软件中的特征。本文提出了一种改进版本的灰狼优化算法（GWO），称为改进的灰狼优化（MGWO）算法。该算法的机制是基于自然界中狼的狩猎机制。该算法选择许多功能，然后将这些功能应用于软件开发生命周期模型，以找出其中的最佳功能。还将该应用程序的结果与常规灰狼优化算法（GWO），改进的二进制蝙蝠算法（MBBAT），改进的鲸鱼优化算法（MWOA）和改进的飞蛾火焰优化（MMFO）进行了比较。