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Feature selection and evaluation for software usability model using modified moth-flame optimization
Computing ( IF 3.7 ) Pub Date : 2020-05-05 , DOI: 10.1007/s00607-020-00809-6 Deepak Gupta , Anil K. Ahlawat , Arun Sharma , Joel J. P. C. Rodrigues
Computing ( IF 3.7 ) Pub Date : 2020-05-05 , DOI: 10.1007/s00607-020-00809-6 Deepak Gupta , Anil K. Ahlawat , Arun Sharma , Joel J. P. C. Rodrigues
This paper introduces a nature-inspired optimized algorithm called modified moth-flame optimization (MMFO) for usability feature selection. To determine quality of software usability plays a significant role. This model contains various usability factors that are divided into several features, which have some characteristics, thus making a hierarchical model. Here, the authors have introduced MMFO (Modified Moth-flame optimization algorithm) for the selection of usability features to get an optimal solution MMFO is an extension of moth-flame optimization algorithm (MFO), which is based on the navigation method of moths called transverse orientation and to the best of our knowledge; this algorithm is introduced in software engineering practices. The selected features and accuracy of proposed MMFO is compared with the original MFO and other related optimization techniques. The results shows that the proposed nature-inspired optimization algorithm outperforms the other related optimizers as it generates a fewer number of selected features and having low accuracy.
中文翻译:
使用改进的飞蛾火焰优化的软件可用性模型的特征选择和评估
本文介绍了一种受自然启发的优化算法,称为改进的飞蛾火焰优化 (MMFO),用于可用性特征选择。确定软件可用性的质量起着重要作用。该模型包含各种可用性因素,这些因素分为几个特征,这些特征具有一些特征,从而构成一个分层模型。在这里,作者引入了MMFO(Modified Moth-flame optimization algorithm)用于选择可用性特征以获得最佳解决方案MMFO是moth-flame optimization algorithm(MFO)的扩展,它基于称为飞蛾的导航方法据我们所知,横向方向;该算法在软件工程实践中被引入。将提出的 MMFO 的选定特征和准确性与原始 MFO 和其他相关优化技术进行比较。结果表明,所提出的自然启发优化算法优于其他相关优化器,因为它生成的选定特征数量较少且精度较低。
更新日期:2020-05-05
中文翻译:
使用改进的飞蛾火焰优化的软件可用性模型的特征选择和评估
本文介绍了一种受自然启发的优化算法,称为改进的飞蛾火焰优化 (MMFO),用于可用性特征选择。确定软件可用性的质量起着重要作用。该模型包含各种可用性因素,这些因素分为几个特征,这些特征具有一些特征,从而构成一个分层模型。在这里,作者引入了MMFO(Modified Moth-flame optimization algorithm)用于选择可用性特征以获得最佳解决方案MMFO是moth-flame optimization algorithm(MFO)的扩展,它基于称为飞蛾的导航方法据我们所知,横向方向;该算法在软件工程实践中被引入。将提出的 MMFO 的选定特征和准确性与原始 MFO 和其他相关优化技术进行比较。结果表明,所提出的自然启发优化算法优于其他相关优化器,因为它生成的选定特征数量较少且精度较低。