Centralized Cooperative Task Preallocation for Heterogeneous UAVs Based on an Improved Artificial Bee Colony Algorithm

In high-intensity modern urban combat environments, centralized task preallocation for heterogeneous multi-unmanned aerial vehicle (UAV) systems is a key technology for enhancing cooperative operational effectiveness. To address the challenges of diverse target types and the limited flexibility in allocation modes, this paper proposes a collaborative task allocation model for heterogeneous UAVs that simultaneously supports two allocation modes: single UAVs engaging multiple targets and multiple UAVs collaboratively striking a single target. The proposed model comprehensively considers key factors such as overall target damage effectiveness, task execution time, and ammunition consumption, with the objective of maximizing the total strike benefit. To efficiently solve this model, a discrete artificial bee colony algorithm incorporating a genetic structure is developed. During the initialization phase, a probability distribution-based greedy initialization is introduced to improve the quality of initial solutions and accelerate convergence. In the search phase, the algorithm integrates a discrete space-based multimodal neighborhood search strategy and a progressive expansion-based genetic learning mechanism, significantly enhancing its global exploration and local exploitation capabilities in complex solution spaces. Simulation results demonstrate that the proposed method can effectively accomplish cooperative multi-target task allocation across various task scales. It achieves higher solution quality and better computational efficiency, exhibiting superior overall performance compared to conventional algorithms.