BALSO-DTV: Binary artificial locust swarm optimization algorithm boosted with agent motion-based dynamic time-varying S-shaped transfer functions for feature selection in high-dimensional data

Key Points

• The study shows significant improvements in feature selection accuracy using a dynamic time-varying transfer function.
• Key metric indicates a 30% increase in performance for high-dimensional data compared to traditional methods.
• This observational analysis employs a binary artificial locust swarm optimization algorithm enhanced by agent motion dynamics.
• These findings highlight the effectiveness of advanced optimization techniques for complex data sets, supporting their broader application.