Optimization is the process of minimizing or maximizing an objective function to find the optimal solution. Metaheuristics is one of the non-deterministic classes of optimization techniques which imitate the natural and social phenomena to solve complex real-world problems. As the count of these algorithms is increasing, the need for reviewing these algorithms also emerges. In this paper, eight popular metaheuristic algorithms are implemented on six real-world benchmark problems. These algorithms are evaluated for different population sizes with fixed iterations. The results have shown that primarily for different population sizes different metaheuristic algorithms have shown best results. But in terms of convergence time, GWO has outperformed in most of the cases.