In recent years, numerous optimizers have emerged and been applied to address engineering design challenges. However, assessing their performance becomes increasingly challenging with growing problem complexity, especially in the realm of real-world large-scale applications. This study aims to fill this gap by conducting a comprehensive comparative analysis of twelve recently introduced metaheuristic optimizers. The analysis encompasses real-world scenarios to evaluate their effectiveness. Initially, a review was conducted on twelve prevalent metaheuristic methodologies to understand their behavior. These algorithms were applied to optimize an automobile structural design, focusing on minimizing vehicle weight while enhancing crash and noise, vibration, and harshness characteristics. To approximate the structural responses, a surrogate model employing radial basis functions was utilized. Notably, the MPA algorithm excelled in automobile design problems, achieving the lowest mass value of 96.90608 kg during both mid-range and long-range iterations, demonstrating exceptional convergence behavior.