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The paper proposes the Promoted Osprey Optimizer (POO) algorithm, inspired by the natural hunting behavior of Ospreys. The algorithm is designed to solve optimization problems by imitating the Osprey’s ability to hunt fish in the water. The algorithm consists of two stages: exploration and exploitation. The exploration stage simulates the Osprey’s initial search for prey, while the exploitation stage simulates the Osprey’s attack on the target. The algorithm also incorporates three techniques: opposition-based learning, chaos map, and randomness. The opposition-based learning technique generates the opposite position alongside the original location within the initial population, improving the search process. The chaos map is used to generate chaotic variables, which enhance the exploration capabilities of the algorithm. The algorithm is validated using the “CEC-BC-2019 test suite” and compared to five established metaheuristic algorithms. The results show that the POO outperforms the other algorithms in terms of both mean fitness value and standard deviation value, indicating its effectiveness and stability. The algorithm has the potential to be a reliable and efficient metaheuristic algorithm for solving a wide range of optimization problems.
