Fractional-Order Black-Winged Kite Algorithm for Moving Target Search by UAV

The nonlocality (capable of associating target dynamics across multiple time moments) and memory properties (able to retain historical trajectories) of fractional calculus serve as the core theoretical approach to resolving the “dynamic information association deficiency” in UAV mobile target search. This paper proposes the Fractional-order Black-winged Kite Algorithm (FOBKA), which transforms the search problem into an adaptability function optimization model aimed at “maximizing target capture probability” based on Bayesian theory. Addressing the limitations of the standard Black-winged Kite Algorithm (BKA), the study incorporates fractional calculus theory for enhancement: A fractional-order operator is embedded in the migration behavior phase, leveraging the memory advantage of fractional-orders to precisely capture the temporal span, spatial position, and velocity evolution of targets, thereby enhancing global detection capability and convergence accuracy. Simultaneously, population individuals are initialized using motion-encoding, and the attack behavior phase combines alternating updates with a Lévy flight mechanism to balance local exploration and global search performance. To validate FOBKA’s superiority, comparative experiments were conducted against eight newly proposed meta-heuristic algorithms across six distinct test scenarios. Experimental data demonstrate that FOBKA significantly outperforms the comparison algorithms in convergence accuracy, operational robustness, and target capture probability.