Design and evaluation of a new tent-shaped transfer function using the Polar Lights Optimizer algorithm for feature selection

This research aims to develop a new transfer function to transform continuous space to binary space using the Polar Lights Optimizer (PLO) algorithm for the feature selection problem. The PLO algorithm relies on simulating the behaviour of the aurora borealis to achieve a balance in exploring and exploiting binary space. A new transfer function called the tent-shaped transfer function has been incorporated into the algorithm to improve its performance. The proposed function was tested on seven datasets, and compared with traditional transfer functions such as the S-shaped function family and the V-shaped function family. The results showed that the tent-shaped transfer function outperforms in terms of feature selection accuracy and reduces the number of features more effectively, which enhances the algorithm's ability to improve performance and reduce computational complexity.