Determination of Optimal Vertiports with Edge Devices Using the Distributed Cuckoo Search Algorithm

Efficient vertiport selection is essential for optimizing route efficiency, reducing travel times, and minimizing operational costs in Urban Air Mobility (UAM) systems. This paper presents a Distributed Cuckoo Search Algorithm (DCSA) leveraging edge devices to dynamically determine optimal vertiports for air taxis operating in urban environments. By distributing computational tasks across edge devices, localized decision-making is enabled, reducing reliance on centralized computing and enhancing adaptability to real-time conditions. Each edge device functions as a computational node, managing a subset of air taxis within its operational zone. The DCSA adapts the nature-inspired Cuckoo Search Algorithm (CSA) to urban air traffic management by incorporating Levy flights to explore broader solution spaces and avoid local minima. Each edge device independently optimizes vertiport assignments by balancing travel distance, landing costs, and waiting times. The multi-objective optimization problem integrates penalty functions to handle constraints like vertiport capacity limits and restricted airspace zones. Periodic communication between edge devices facilitates global convergence by sharing best solutions, enabling dynamic refinement and adaptation to changing traffic patterns. The Distributed Particle Swarm Optimisation (DPSO) has been inmplemented as a benchmark and the simulation results showed better performance of the DCSA inn terms of total cost and convergence, while the vertiport utilisation showed more even configuration of the DPSO. In terms of execution times the difference between the two algorithms is negligible.