Multi-UAV Task Allocation Based on Improved Particle Swarm Optimization

This paper mainly studies the task allocation problem in multi-UAV ground-to-ground coordinated operations, and establishes a corresponding mathematical model, which takes into account constraints such as time windows and tracks. Through the analysis of the mission scenario, this study conducts a basic theoretical analysis of the particle swarm algorithm, and balances the development and exploration capabilities of the algorithm by integrating the harmony search algorithm. At the same time, the golden sine strategy is introduced to increase the randomness of the search method. A particle swarm algorithm based on the golden sine harmony search is proposed, and the random search method is used to determine the value of the key control parameters. Finally, the effectiveness of the task allocation model and the superiority of the improved algorithm are verified by comparative simulation experiments. The simulation results show that the algorithm proposed in this paper shows higher convergence accuracy and robustness when dealing with the multi-UAV task allocation problem under the time window constraint.