Collaborative multi-task assignment of heterogeneous UAVs based on hybrid strategies based multi-objective particle swarm

Aiming at the problem of collaborative multi-task assignment of heterogeneous UAVs under multiple constraints, a three-objective optimization model was constructed, which considered the UAV flight distance cost, time cost, combat effectiveness and multiple constraints. A multi-objective particle swarm optimization algorithm based on hybrid strategies was proposed to solve the model. An average action efficiency index of ammunition was proposed to evaluate the task strike efficiency, and considering the possibility of deadlock during task execution, a calculation of waiting time was proposed in the process of modeling. In order to solve the problem that traditional particle swarm optimization falls into local optimality, and ensure that feasible solutions satisfying constraints are searched, a constraint-based particle dynamic optimal initialization strategy, a dominance relationship-based advantageous individual selection strategy, and a task-based small module particle update and correction strategy were proposed, respectively. The overall performance of the algorithm in terms of convergence accuracy and diversity was effectively improved by these strategies. The validity of the model and the algorithm was verified through multi-scenario simulation experiments and ablation experiments. Results show that the solution sets obtained by the proposed algorithm are more convergent, diverse and evenly distributed than the comparative algorithms, and the collaborative multi-task assignment of heterogeneous UAVs is efficiently realized by the proposed algorithm. © 2025 Zhejiang University. All rights reserved.