There are various noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. These are iterative search processes that efficiently perform the exploration and exploitation in the solution space, aiming to efficiently find near optimal solutions. Considering the solution space in a specified region, some models contain global optimum and multiple local optima. In this context, two types of meta-heuristics called Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. Firefly Algorithm is one of the recent evolutionary computing models which is inspired by fireflies behavior in nature. PSO is population based optimization technique inspired by social behavior of bird flocking or fish schooling. A series of computational experiments using each algorithm were conducted. The results of this experiment were analyzed and compared to the best solutions found so far on the basis of mean of execution time to converge to the optimum. The Firefly algorithm seems to perform better for higher levels of noise.