Enhanced Black-Winged Kite Optimization for Optimal UAV Path Planning in Urban Environments

This paper addresses the problem of UAVs safely approaching targets under optimal path conditions in urban environments. An enhanced Black-Winged Kite Algorithm, namely OLBKA, is proposed. The proposed algorithm integrates a random opposition-based learning strategy with an adaptive Levy flight strategy to boost the algorithm's global search capability. The random opposition-based learning strategy improves the quality of the initial solutions within the population, while the adaptive Levy flight strategy allows the algorithm to escape local optima. OLBKA is validated through two examples, showcasing rapid convergence and superior search capabilities. Overall, the algorithm significantly enhances UAV path planning in urban environments.