With the rapid proliferation of available data in numerous fields, the metaheuristic method has become popular in solving optimization problems in daily life aspects. The metaheuristic method is based on the inspiring heuristic method, independent of the type of the problem. Most real-world optimization and search problems involve large dimensionality, discontinuities, non-linearities, and non-convexity. Addressing such real-world problems through a mathematically motivated model to find a good solution within a reasonable time has been accomplished by metaheuristic computing. Metaheuristic methods ensure the near-optimal solution at a limited computation time even though it lacks the exact optimal solution for the search and optimization problems. With the ease of implementation, metaheuristic methodologies have gained significant attention among multiple application domains. By resembling the physical, biological, or natural principle of insects, animals, or other living beings, metaheuristic methods have applied the computations for the search and optimization process. Nowadays, several new bio-inspired metaheuristic algorithms have been developed to handle real-time problems, including the Artificial Bee Colony Algorithm (ABC), Artificial Fish School Algorithm (AFS), Bat Algorithm(BA), Cuckoo Search (CS), Firefly Algorithm (FA), and others.