Optimization problems are of prime importance for industrial as well as the scientific community in diverse disciplines. There are various optimization problems that present traits like high non-linearity .and multimodality, the solution of which is usually complex. Further, in many cases, some intricate optimization problems present noise and/or discontinuities which make conventional deterministic methods inefficient to obtain global solutions. Thus, global optimization approaches based on metaheuristics provide a viable alternative for solving complex optimization problems and do not require any information regarding the properties of the objective function. Metaheuristic algorithms are nowadays attaining popularity among researchers extensively owing to their success to find the optimum solution and steadfastness in solving real-world problems. They are generally utilized to solve several complex problems in science and engineering, finance as well as management. Metaheuristic algorithms have four inherent properties. First of all, they are primarily motivated by natural phenomena in the universe. Secondly, they constitute a stochastic method wherein an optimum solution is not guaranteed and is randomly searched in a feasible search space. Moreover, they do not require the derivative term to obtain the maximum or the minimum value. Last but not the least; they are always associated with some user-defined values at the initial stage to function suitably. Therefore this work gives the detail review on metaheuristic algorithms for optimizing fractional order controller parameters.