Equitable task allocation method for multi-UAVs urban logistics

A multi-strategy improved hippopotamus optimization (MIHO) algorithm was proposed to develop a balanced multi-UAVs task allocation model for urban logistics delivery. The model is defined with economic cost, delay penalty, and load-balance safety risk as optimization objectives. Latinized stratified sampling was adopted to enhance population diversity, a sine-cosine strategy was introduced to strengthen global search capability, and a simulated annealing mechanism was integrated to improve solution accuracy and avoid premature convergence. Simulation results show that the constructed model can effectively satisfy urban logistics distribution demands. For the task-allocation problem, the MIHO algorithm achieved a best fitness, mean fitness, and fitness standard deviation of 0.0380, 0.0689 and 0.0159, respectively-corresponding to decreases of 56.32%, 47.36% and 26.73% compared with the original hippopotamus optimization (HO) algorithm