Currently, certain complex issues are classified as NP-hard problems, for which there is no exact solution, or they cannot be solved in a reasonable amount of time. As a result, metaheuristic algorithms have been developed as an alternative. These algorithms aim to approximate the optimal solution rather than providing a definitive one. Over recent years, these algorithms have gained considerable attention from the research community. Nature and its inherent principles serve as the primary inspiration for the development of metaheuristic algorithms. A notable subgroup of these algorithms is evolutionary algorithms, which are modeled based on the behavior of social and intelligent animals and organisms. However, each metaheuristic algorithm typically excels only with specific types of problems. As a result, researchers continuously endeavor to develop new algorithms. This study introduces a novel metaheuristic algorithm known as the Farmer Ants Optimization Algorithm (FAOA). The algorithm is inspired by the life of farmer ants, which cultivate mushrooms for food, protect them from pests, and nourish them as they grow. These behaviors, based on their social dynamics, serve as the foundation for the proposed algorithm. Experiments conducted on various engineering and classical problems have demonstrated that the FAOA provides acceptable solutions for discrete optimization problems.