A major challenge in applying metaheuristic algorithms is their sensitivity to parameter tuning, premature convergence, and computational cost, particularly when dealing with complex real-world constraints. This study systematically assesses these factors by analyzing convergence behavior, solution quality, and computational efficiency among some common algorithms. Results show that Laplacian Biogeography Based Optimization (LX-BBO) consistently achieves superior performance regarding optimal function values and stability, outperforming Marine Predator Algorithm (MPA) and Sine Cosine algorithm (SCA) in most cases. The findings provide empirical evidence supporting the suitability of LX-BBO for constrained engineering optimization tasks while highlighting the conditions under which MPA and SCA can be effective. This research benefits the domain by performing systematic comparisons and assisting in algorithm selection for practical engineering problems.