paper14

Isuwa Jeremiah, Mohammed Abdullahi, Sahabi Ali Yusuf, and Ibrahim Hayatu Hassan

Keywords:Swarm Intelligence, Particle Swarm Optimization, Feature Selection, Dimensionality Reduction

Abstract

Over the years, scientists have used natural discoveries such as evolution to solve real-world problems. Addressing
the challenges that arise when dealing with high-dimensional data is one such problem. These challenges include
difficulties in analyzing, visualizing, and modelling these high-dimensional data. As a result, the Swarm Intelligence
(SI) technique was developed, which was inspired by natural swarm foraging behaviors. Particle swarm optimization
(PSO) is a well-known SI algorithm for addressing a wide range of optimization problems. As a result, it has been
used to solve a variety of optimization problems in fields as diverse as genomic analysis and intrusion detection
systems. One of the most successful areas of PSO application is feature selection, which entails using computational
techniques to select a reduced subset of features that have a sufficient relationship with their corresponding class
labels. This, in turn, addresses the mentioned challenges. Nonetheless, progressive research has revealed several
problems with PSO, including problems with diversity, and premature convergence among others. As a result, several
improvements and extensions were made to various aspects of the algorithm since its inception to make it efficient.
This paper organizes and summarizes current research on improvements to the PSO algorithm for solving the feature
selection problem. Consequently, it presents current trends and directions for scholars in the field, as well as open
challenges and literature gaps to investigate.