<p>In this paper, a new nature-inspired meta-heuristic algorithm, the Leech Growth Algorithm (LGA), is proposed and thoroughly tested to provide an alternative optimization method for solving practical engineering problems. LGA is inspired by the developmental processes of leeches in nature, including random movements, foraging, nesting, and mating behavior. A unique maturation factor is introduced to simulate the developmental process of leeches, while the maturation factor is also used to balance exploration and exploitation. In the exploration phase of LGA, leeches randomize movement and foraging; in the exploitation phase, leeches nesting, and mating. At the same time, the maturation factor of the leeches causes LGA to shift from exploration to exploitation. In addition, theoretical analysis and experiments have led to the conclusion that the maturation factor can be used as a flexible and practical new threshold conversion mechanism for meta-heuristic algorithms. LGA is experimentally and analytically compared with other excellence optimizers through experimental and analytical comparisons on 41 benchmark functions as well as 4 engineering problems and 1 scheduling problem. The results show that LGA outperforms the tested competitors in solving benchmark functions and engineering problems in general, validating the utility of the proposed optimizer in solving challenging real-world problems.</p>