Particle Swarm Optimization (PSO) algorithm is widely acknowledged for its robust performance in solving optimization problems within engineering and scientific domains. This iterative method enhances solutions by evaluating them against specific metrics or fitness criteria. Weightless Swarm Algorithm (WSA), an advanced variant of PSO, demonstrates accelerated convergence and enhanced accuracy. In this study, WSA was compared with four alternative algorithms while also analyzing the influence of social parameters on WSA’s performance. Simulation results indicate that WSA performed similarly or superior in seven of eight benchmark problems. Furthermore, WSA exhibited reduced computation time and faster processing speed across all test cases. These findings suggest that WSA is a competitive algorithm for optimization problems, particularly when speed and memory are critical considerations.