This chapter provides a review of meta-heuristic algorithms for the optimization in various fields of knowledge ranging from math to computer science, management to finance and engineering to industrial sectors, as all require solving complex optimization problems. So far, researchers have tried to present a vast array of potential solutions that either reduce or maximize the complexity of the optimization problem. However, while selecting such an algorithm, it is difficult to find an unambiguous optimum, resulting in time-consuming investments. Meta-heuristic algorithms are at the forefront, curbing the vastness of the search space with the judicious use of creativity and computing power. Within the realm of population-based methods, a group of agents work together to accelerate convergence. Nevertheless, many algorithms exist for achieving global optimization, each has its own set of advantages and disadvantages. In this chapter, we aim to perform an in-depth comparison of population-based meta-heuristics algorithms to examine the dialectical relationship of benefits and drawbacks associated to each approach.