Dual-Population Evolution-Based Collaborative Optimization for Coordinated UAVs Mission Patrol Under Mountainous Environments

Uncrewed aerial vehicles (UAVs) play a crucial role in visiting task points to support sector surveillance and maintain environmental and operational stability in mountainous mission patrols. Completing UAV patrol missions requires determining an optimal sequence for visiting task points and planning feasible paths to ensure the UAV safety in the presence of diverse peaks, valleys, and infeasible regions in mountainous environments. To address these issues, we propose a dual-population evolution-based collaborative optimization algorithm (DECOA) for coordinated UAV mission patrol, encompassing both UAV task assignment and path planning, in mountainous environments. First, we design a comprehensive model to represent mountainous environments accurately. Then, DECOA is proposed to address the UAV mission patrol optimization problem using a population-evolution framework applied to the constructed model. In DECOA, two distinct particle encoding schemes are introduced to efficiently convert task assignment and path planning solutions in UAV mission patrol models into representations suitable for optimization processing. Additionally, a dynamic dual-population search strategy is devised to balance exploration and exploitation capabilities. To enhance the global search capability of DECOA in promising regions, an adaptive collaboration mechanism is proposed. Furthermore, to evaluate the performance of DECOA, various scenarios are designed to conduct extensive experiments, focusing on UAV task assignment, path planning, and coordinated mission patrol. The experimental results demonstrate the superiority of the proposed algorithm and validate the effectiveness of the designed test scenarios.