Research Area: Metaheuristic Computing
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.
Keywords:
nature-inspired optimization
feature selection
hierarchical model
Moth-flame optimization
low accuracy
Author(s) Name: Deepak Gupta, Anil K. Ahlawat, Arun Sharma & Joel J. P. C. Rodrigues
Journal name: Computing
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s00607-020-00809-6
Volume Information: Volume 102
