Multiobjective Variable Neighborhood Descent for Heterogeneous Multi-UAV Coordinated Scheduling

Efficient scheduling is critical for the effective use of heterogeneous unmanned aerial vehicles (UAVs) equipped with various sensors. The collaborative "electronic signal guided imaging" reconnaissance mode is first discussed in this article, where electronic signal reconnaissance equipment is initially used to locate electromagnetic targets over long distances, offering approximate target locations that can guide the next imaging reconnaissance. We construct a multiobjective optimization model for heterogeneous multi-UAV coordinated scheduling that minimizes the total travel cost and maximizes the overall weight of scheduled tasks simultaneously. A local-search-based multiobjective algorithm, called multiobjective variable neighborhood descent, is proposed to solve the problem. Specifically, a customized variable neighborhood descent is designed to optimize each objective in parallel, which contains several sophisticatedly designed problem-specific heuristics selected by adaptive weight adjustment. In addition, Metropolis acceptance criteria, which employ adaptive temperature control, are incorporated to prevent premature convergence and improve optimization capability. Furthermore, knee solutions and boundary solutions are optimized specifically to further enhance the optimization. Various local search strategies for two objectives are implemented in an iterative manner until the predefined stopping criteria are satisfied. The proposed algorithm is tested on 18 benchmark instances and one real-world instance. The experimental results on benchmark instances show the superiority of our proposed algorithm with regard to diversity and convergence compared with seven effective algorithms, especially for large-scale instances. The effectiveness and practicability of our proposed algorithm are verified by an additional result on a real-world instance.