Adaptive crow search algorithm based on Levy flight

To address the shortcomings of the Crow Search Algorithm (CSA), such as its tendency to fall into local optima and the blindness of its location update strategy, this paper proposes an Adaptive Crow Search Algorithm (ACSA) based on Lévy flight. The proposed algorithm dynamically adjusts perception probability and flight distance while optimizing the location update strategy. By integrating Lévy flight, an experience factor, and adaptive parameter mechanisms, ACSA enhances global exploration in early iterations and local exploitation in later stages. Experimental validation on eight benchmark functions demonstrates the algorithm's superiority over state-of-the-art optimization methods in terms of average optimization results, standard deviation, convergence speed, and robustness.