A quantum-enhanced heuristic algorithm for optimizing aircraft landing problems in low-altitude intelligent...

Area of Science:

• Aerospace Engineering
• Quantum Computing
• Operations Research

Background:

• The expansion of the low-altitude economy necessitates efficient scheduling for manned aerial vehicles.
• Aircraft landing time window optimization is a complex, high-dimensional problem prone to local optima.
• Existing optimization methods struggle with the complexity and scale of these scheduling tasks.

Purpose of the Study:

• To develop a novel optimization algorithm for the aircraft landing time window problem.
• To enhance global search capabilities and escape local optima in complex optimization tasks.
• To improve the efficiency and precision of landing schedule optimization for low-altitude manned aerial vehicles.

Main Methods:

• Development of a Quantum-Enhanced Whale Optimization Algorithm (QEWOA).
• Integration of a quantum random number generator to improve initial population diversity.
• Incorporation of an enhanced quantum tunneling mechanism to escape local optima.
• Hybridization with the Artificial Bee Colony algorithm to bolster global and local search.

Main Results:

• QEWOA demonstrates significantly improved global search ability.
• The algorithm achieves higher optimization precision compared to traditional methods.
• QEWOA exhibits enhanced convergence speed in solving complex scheduling problems.
• Experimental validation confirms superior performance in optimizing landing time windows.

Conclusions:

• The Quantum-Enhanced Whale Optimization Algorithm (QEWOA) offers a robust solution for optimizing aircraft landing schedules.
• QEWOA effectively overcomes the limitations of local optima and enhances search efficiency.
• This approach provides a significant advancement for managing low-altitude manned aerial vehicle operations.