HOA-OBL: hybrid opposition-based hippopotamus optimization framework for efficient UAV task allocation

Unmanned aerial vehicles (UAVs) are widely deployed in public and civic areas, especially in environments where human presence is restricted due to safety concerns. UAVs play a vital role in maximizing resource utilization and minimizing operational redundancy in applications such as disaster recovery, infrastructure inspection, and surveillance. However, the conventional task-allocation frameworks often suffer from suboptimal resource utilization and limited adaptability in dynamic operational environments. To resolve these problems, this paper presents a hybrid opposition based Hippopotamus Optimization (HOA-OBL) algorithm framework for efficient UAV task allocation optimization. Particularly, Hippopotamus Optimization Algorithm (HOA) is one of the widely known population-based algorithms, motivated by the inherent behavior of hippopotamuses, but it has a limitation of local optima in certain iterations. To enhance the exploration, the Opposition-Based Learning (OBL) is integrated with HOA to explore the search space to prevent it from converging too early to local optima. We performed simulation experiments in dynamic environments with 20 UAVs and 100 tasks. The experimental results imply that the proposed framework achieves reductions of about 17% to 33% in energy consumption and performance enhancements of about 33% to 50% in load balancing when compared with existing models such as HOA, GA, PSO, and GWO. The results demonstrate that the proposed framework provides a more balanced and scalable approach to task assignment to UAVs, thereby improving efficiency and consistency in real-world UAV missions.