# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 5 Simulation
### Experimental Setup
#### 5.1.2 Comparison Algorithms  
1. Greedy Algorithm: The Greedy Algorithm is a simple constructive algorithm. In the dynamic scenario described in this paper, the steps to construct a feasible solution are as follows: anchor point selection, intra-anchor point route partitioning, UAV route allocation, and vehicle route planning. In the anchor point selection step, appropriate positions are selected as anchor points according to specific rules. These anchor points serve as the key hubs for the cooperation between vehicles and UAVs. Subsequently, the intra-anchor point route partitioning is carried out. The task points corresponding to each anchor point are reasonably planned to form the flight routes of UAVs. Next, according to the performance of UAVs and task requirements, the partitioned routes are allocated to different UAVs to complete the UAV route allocation. Finally, based on the UAV route allocation results, the vehicle's driving route is planned to ensure that the vehicle can efficiently transport UAVs to various task areas.
2. Hybrid Genetic Algorithm (HGA): In [43], the Hybrid Genetic Algorithm (HGA) was used to study the multi-UAV vehicle delivery problem in static scenarios. This method first generates an initial solution with the Greedy Algorithm. Although this initial solution may not be the optimal one, it is feasible. Subsequently, the Hybrid Genetic Algorithm is used to optimize it. The Hybrid Genetic Algorithm combines the global search ability of the Genetic Algorithm with local search techniques. By simulating the selection, crossover, andmutation operations in natural evolution, the initial solution is continuously improved, and then UAVs and vehicles are guided to execute tasks. However, since this research did not involve dynamic factors, in the dynamic task-processing context of this experiment, the HGA was reapplied to generate solutions for the unexecuted task points. That is, whenever the state of task points changed (such as UAV speed fluctuations, the emergence of new task points, etc.), the HGA was restarted to enable it to adapt to the dynamic environment and optimize the task execution plan.
3. Static Optimization Algorithm (SOA): The Static Optimization Algorithm (SOA) is a deterministic method that does not consider dynamic changes in the environment. In the scenario set in this paper, the steps to construct a feasible solution are similar to those of other algorithms, including anchor point selection, intra-anchor point route partitioning, UAV route allocation, and vehicle route planning. However, it is completely different from the dynamic optimization method. The SOA cannot cope with the speed fluctuations of UAVs and vehicles. It provides a static solution and does not make dynamic adjustments according to environmental changes. This means that once the initial anchor points, routes, and task-allocation plans are determined, no matter how the speeds of UAVs and vehicles change subsequently, the SOA will not modify the plan. This may lead to a decrease in task execution efficiency and make it difficult to meet the task requirements in dynamic scenarios.