Optimization is necessary for ensuring the quality of every software system. While solving optimization problems, it's a challenge to find optimal solutions from a huge number of solutions. Various types of optimization techniques have been developed to reduce complexities in optimization. Metaheuristic algorithms have been very effective in handling optimization problems. There are many efficient metaheuristic algorithms for solving optimization problems. However, for solving different optimization problems, different algorithms are suitable. It's important to evaluate every metaheuristic algorithm's performance. Optimization efficiencies of many metaheuristic algorithms haven't been fully analyzed yet. Therefore, this paper has evaluated the optimization performance of a slightly adapted Firefly Algorithm against some existing metaheuristic strategies like Bat Algorithm, Bacteria Foraging Algorithm, and Cuckoo Search Algorithm. The evaluation has been done using 11 benchmark functions. The results after the implementation have shown that Firefly Algorithm performs competitively against other metaheuristic algorithms in terms of finding optimal solutions.