Research on Task Allocation for Multiple UAVs Based on a Hybrid BA-PSO Algorithm

To address the shortcomings of the PSO algorithm, i.e., premature convergence and a tendency to fall into local optima, a collaborative particle regeneration strategy is introduced to help particles escape local optima. The principle of this strategy is as follows: if a particle in the population is detected to have not been updated for several iterations, information from a “leader” and a “follower” in the population is used to guide the particle out of the local optimum. Furthermore, to balance the global and local search capabilities of particles, the velocity update mechanism of the Bat Algorithm (BA) is incorporated, enabling particles to fully explore the solution space in the early stage and then quickly approach the optimal solution in the later stage. Simulation comparison experiments on the CEC 2017 benchmark suite demonstrate that the proposed improved PSO algorithm, combining these two enhancements, outperforms several other algorithms. In a task allocation simulation example, the proposed algorithm achieves an optimal fitness value of 170.89, verifying its efficiency and robustness under complex constraints.