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As the low-altitude economy expands, optimizing the landing schedules of manned aerial vehicles has emerged as a critical challenge. The aircraft landing time window problem is a complex, high-dimensional optimization task frequently hindered by local optima. This study presents an Quantum-Enhanced Whale Optimization Algorithm (QEWOA) that incorporates quantum computing to tackle these challenges. By utilizing a quantum random number generator, the algorithm improves the diversity of the initial population, preserving potential optimal solutions for a more effective global search. Additionally, the incorporation of an enhanced quantum tunneling mechanism enables the algorithm to escape local optima and perform more extensive searches. The combination of the Artificial Bee Colony algorithm and Whale Optimization further strengthens both global search and local optimization capabilities. Experimental results demonstrate that QEWOA significantly improves global search ability, optimization precision, and convergence speed, surpassing traditional methods in optimizing landing time windows for low-altitude manned aerial vehicles.
Lu et al. (Tue,) studied this question.