Bio-inspired computing-based techniques are gaining importance in the current engineering research domain, particularly for solving complex optimization problems. These are stochastic search techniques which are developed to achieve near-optimal solutions to large-scale optimization problems. The conventional mathematical optimization methods often fail as the solutions are trapped in local optima, which gave rise to the development of alternate derivative free metaheuristic global optimization techniques. In recent times, the bio-inspired algorithms have been explored for various applications and their performance is as good as or better than that of conventional techniques.