Energy Consumption Optimization of Multi-Trip UAV Routing Using Surrogate Modeling with Heuristic and Metaheuristic Algorithms

Unmanned aerial vehicle (UAV) routing under realistic operational conditions requires simultaneous consideration of distance- and payload-dependent energy consumption, limited battery capacity, and multi-trip mission feasibility—factors that are rarely integrated into a unified, reproducible benchmarking framework. This study proposes an energy-aware, multi-trip UAV routing model for single-warehouse cargo delivery operations, in which total energy consumption is minimized through a second-degree polynomial power function derived from empirical motor thrust–power data of a theoretically designed quadrotor UAV with a maximum payload capacity and a usable battery capacity. Euclidean service locations and loads are generated randomly within a continuous operational domain to reflect spatial uncertainty, and a split-based decoding mechanism enforces battery feasibility constraints throughout the route. Twenty-six heuristic and metaheuristic algorithms sourced from the recent UAV routing literature are implemented within a standardized MATLAB benchmarking environment and evaluated on TSPLIB instances (Berlin52, kroA100), as well as randomly generated instances with different numbers of delivery locations. A refined subset of eight representative algorithms is subjected to comprehensive scalability analysis under both distance- and energy-minimization objectives, separately. The findings provide evidence-based guidelines for algorithm selection across offline planning and real-time UAV routing scenarios, and establish a transparent, reproducible benchmark baseline for energy-constrained single-UAV operations.