This paper introduces the Greater Cane Rat Algorithm (GCRA), a new nature-inspired metaheuristic technique for solving optimization problems. The GCRA is inspired by the intelligent foraging behaviors of greater cane rats during and off the mating season. The algorithm's exploration phase is achieved through the rats' departure from shelters to forage and leave trails, while the exploitation phase is facilitated by the alpha male maintaining knowledge of these trails, allowing other rats to adjust their positions accordingly. The performance of GCRA is evaluated using 22 classical benchmark functions, 10 CEC 2020 complex functions, 22 real-world optimization problems from the CEC 2011 suite, and six classical engineering problems. The results show that GCRA produces optimal or nearly optimal solutions and avoids getting trapped in local minima, outperforming ten state-of-the-art algorithms in terms of computational efficiency and convergence stability. The GCRA source code is publicly available, making it a valuable tool for global optimization tasks.This paper introduces the Greater Cane Rat Algorithm (GCRA), a new nature-inspired metaheuristic technique for solving optimization problems. The GCRA is inspired by the intelligent foraging behaviors of greater cane rats during and off the mating season. The algorithm's exploration phase is achieved through the rats' departure from shelters to forage and leave trails, while the exploitation phase is facilitated by the alpha male maintaining knowledge of these trails, allowing other rats to adjust their positions accordingly. The performance of GCRA is evaluated using 22 classical benchmark functions, 10 CEC 2020 complex functions, 22 real-world optimization problems from the CEC 2011 suite, and six classical engineering problems. The results show that GCRA produces optimal or nearly optimal solutions and avoids getting trapped in local minima, outperforming ten state-of-the-art algorithms in terms of computational efficiency and convergence stability. The GCRA source code is publicly available, making it a valuable tool for global optimization tasks.