This paper introduces a novel bio-inspired meta-heuristic optimization algorithm, named termite life cycle optimizer (TLCO), which is based on both the life cycle of a termite colony and the modulation of movement strategies used by many animal species in nature. Termite colonies are comprised of three distinct castes: the workers, the soldiers and the reproductive termites. Each caste undertakes a set of specific tasks that ensure the growth and survival of the colony. TLCO mimics the activities of these three castes that are implemented in a mathematical model. The model is then used to find the global optimum in classic optimization problems. First, the behaviors of the workers, soldiers and reproductive termites are used to simulate a balance between the tasks of exploration and exploitation. Second, the initial population securely records the information obtained at each iteration and transmits it to workers and soldiers at the next iteration. This process is repeated until the global optimum is found with the smallest error. Besides, a new proposed function combined with Lévy flight is used to modulate the movement of termites that increases its flexibility. Thus, TLCO can cover both long distances during the first iterations to improve the convergence rate and shorter distances during the last iterations to enhance the level of accuracy. We then compare the performances of TLCO with other well-known nature-inspired algorithms using 23 classical benchmark functions, CEC2005 benchmark functions, and five real engineering design problems. The results demonstrate the effectiveness and reliability of TLCO in solving these optimization problems. Source codes of TLCO is publicly available at http://goldensolutionrs.com/termite-life-cycle-optimizer.html.