The paper introduces the Greater Cane Rat Algorithm (GCRA), a nature-inspired metaheuristic for optimization problems. Inspired by the foraging behavior of greater cane rats during and off mating seasons, GCRA models the intelligent trails left by these animals, which lead to food, water, and shelter. The algorithm's design is based on the alpha male's knowledge of these trails, which other rats use to adjust their positions. During mating season, foraging is concentrated in areas with abundant food, aiding exploitation. GCRA is tested on 22 classical benchmark functions, 10 CEC 2020 complex functions, and 22 real-world CEC 2011 problems, as well as six engineering problems. The results show that GCRA consistently finds optimal or near-optimal solutions, outperforming other algorithms in terms of convergence and stability. The algorithm's performance is validated using statistical tests, and it is found to be effective in avoiding local minima. GCRA's mathematical model is flexible, with only one parameter to tune, and it is efficient in solving both constrained and unconstrained optimization problems. The algorithm's exploration and exploitation phases are balanced, making it a competitive tool for complex optimization tasks. The study concludes that GCRA is a promising metaheuristic for real-world optimization problems.The paper introduces the Greater Cane Rat Algorithm (GCRA), a nature-inspired metaheuristic for optimization problems. Inspired by the foraging behavior of greater cane rats during and off mating seasons, GCRA models the intelligent trails left by these animals, which lead to food, water, and shelter. The algorithm's design is based on the alpha male's knowledge of these trails, which other rats use to adjust their positions. During mating season, foraging is concentrated in areas with abundant food, aiding exploitation. GCRA is tested on 22 classical benchmark functions, 10 CEC 2020 complex functions, and 22 real-world CEC 2011 problems, as well as six engineering problems. The results show that GCRA consistently finds optimal or near-optimal solutions, outperforming other algorithms in terms of convergence and stability. The algorithm's performance is validated using statistical tests, and it is found to be effective in avoiding local minima. GCRA's mathematical model is flexible, with only one parameter to tune, and it is efficient in solving both constrained and unconstrained optimization problems. The algorithm's exploration and exploitation phases are balanced, making it a competitive tool for complex optimization tasks. The study concludes that GCRA is a promising metaheuristic for real-world optimization problems.