Gesture recognition plays a critical role in applications ranging from gaming and remote manipulation to assistive technologies for individuals with disabilities. Force myography (FMG) has recently emerged as a promising approach for gesture recognition due to its strong classification capabilities. In this study, eleven American Sign Language (ASL) gestures were classified using FMG signals. To improve classification accuracy, three advanced swarm-based feature selection algorithms: the Binary Gray Wolf Optimizer (BGWO), Binary Grasshopper Optimization Algorithm (BGOA), and Binary Hybrid Gray Wolf Particle Swarm Optimizer (BGWOPSO), were evaluated as wrapper methods for an Extreme Learning Machine (ELM) classifier. Ten volunteers contributed data using a bracelet equipped with six force-sensitive resistor (FSR) sensors. Results show that the BGWOPSO-based feature selection approach significantly outperformed the other methods, increasing ELM classification accuracy from 55.82% to 91.36%.